--- comments: true --- # General Table Recognition V2 Pipeline Usage Tutorial ## 1. Introduction to General Table Recognition v2 pipeline Table recognition is a technology that automatically identifies and extracts table content and its structure from documents or images. It is widely used in fields such as data entry, information retrieval, and document analysis. By using computer vision and machine learning algorithms, table recognition can convert complex table information into an editable format, making it easier for users to further process and analyze data. The General Table Recognition v2 pipeline (PP-TableMagic) is designed to tackle table recognition tasks, identifying tables in images and outputting them in HTML format. Unlike the original General Table Recognition pipeline, this version introduces two new modules: table classification and table cell detection. By adopting a multi-model pipeline combining "table classification + table structure recognition + cell detection", it achieves better end-to-end table recognition performance compared to the previous version. Based on this, the General Table Recognition v2 pipeline natively supports targeted model fine-tuning, allowing developers to customize it to varying degrees for satisfactory performance in different application scenarios. Furthermore, the General Table Recognition v2 pipeline also supports end-to-end table structure recognition models (e.g., SLANet, SLANet_plus, etc.) and allows independent configuration for wired and wireless table recognition methods, enabling developers to freely select and combine the best table recognition solutions. This pipeline is applicable in a variety of fields, including general, manufacturing, finance, and transportation. It also provides flexible service deployment options, supporting multiple programming languages on various hardware. Additionally, it offers capabilities for secondary development, allowing you to train and fine-tune your own datasets based on this pipeline, with the trained models seamlessly integrated.

The General Table Recognition Pipeline v2 includes the following 8 modules. Each module can be trained and inferred independently and contains multiple models. For detailed information, please click on the corresponding module to view the documentation. - [Table Structure Recognition Module](../module_usage/table_structure_recognition.md) - [Table Classification Module](../module_usage/table_classification.md) - [Table Cell Detection Module](../module_usage/table_cells_detection.md) - [Text Detection Module](../module_usage/text_detection.md) - [Text Recognition Module](../module_usage/text_recognition.md) - [Layout Region Detection Module](../module_usage/layout_detection.md) (optional) - [Document Image Orientation Classification Module](../module_usage/doc_img_orientation_classification.md) (optional) - [Text Image Unwarping Module](../module_usage/text_image_unwarping.md) (optional) In this pipeline, you can choose the models to use based on the benchmark data below. > The inference time only includes the model inference time and does not include the time for pre- or post-processing.

Table Structure Recognition Module Models:

Model	Model Download Link	Accuracy (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
SLANet	Inference Model/Training Model	59.52	23.96 / 21.75	- / 43.12	6.9	SLANet is a table structure recognition model developed by Baidu PaddlePaddle's vision team. This model significantly improves the accuracy and inference speed of table structure recognition by using a CPU-friendly lightweight backbone network PP-LCNet, a high-low feature fusion module CSP-PAN, and a feature decoding module SLA Head that aligns structure and location information.
SLANet_plus	Inference Model/Training Model	63.69	23.43 / 22.16	- / 41.80	6.9	SLANet_plus is an enhanced version of the SLANet table structure recognition model developed by Baidu PaddlePaddle's vision team. Compared to SLANet, SLANet_plus significantly improves the recognition capabilities for wireless tables and complex tables, while reducing the model's sensitivity to table positioning accuracy, allowing for accurate recognition even if the table is slightly misaligned.
SLANeXt_wired	Inference Model/Training Model	69.65	85.92 / 85.92	- / 501.66	351	The SLANeXt series is a new generation of table structure recognition models developed by Baidu PaddlePaddle's vision team. Compared to SLANet and SLANet_plus, SLANeXt focuses on recognizing table structures and has been trained with dedicated weights for recognizing wired and wireless tables, significantly enhancing recognition capabilities across both types, especially for wired tables.
SLANeXt_wireless	Inference Model/Training Model	69.65	85.92 / 85.92	- / 501.66	351

Table Classification Module Models:

Model	Model Download Link	Top1 Acc (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)
PP-LCNet_x1_0_table_cls	Inference Model/Training Model	94.2	2.62 / 0.60	3.17 / 1.14	6.6

Table Cell Detection Module Models:

Model	Model Download Link	mAP (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
RT-DETR-L_wired_table_cell_det	Inference Model/Training Model	82.7	33.47 / 27.02	402.55 / 256.56	124	RT-DETR is the first real-time end-to-end object detection model. The Baidu PaddlePaddle vision team based RT-DETR-L as the base model, completing pre-training on a self-built table cell detection dataset, achieving good performance in detecting both wired and wireless table cells.
RT-DETR-L_wireless_table_cell_det	Inference Model/Training Model	82.7	33.47 / 27.02	402.55 / 256.56	124

Text Detection Module Models:

Model	Model Download Link	Detection Hmean (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PP-OCRv5_server_det	Inference Model/Training Model	83.8	89.55 / 70.19	383.15 / 383.15	84.3	PP-OCRv5 server-side text detection model with higher accuracy, suitable for deployment on high-performance servers
PP-OCRv5_mobile_det	Inference Model/Training Model	79.0	10.67 / 6.36	57.77 / 28.15	4.7	PP-OCRv5 mobile-side text detection model with higher efficiency, suitable for deployment on edge devices
PP-OCRv4_server_det	Inference Model/Training Model	69.2	127.82 / 98.87	585.95 / 489.77	109	PP-OCRv4 server-side text detection model with higher accuracy, suitable for deployment on high-performance servers
PP-OCRv4_mobile_det	Inference Model/Training Model	63.8	9.87 / 4.17	56.60 / 20.79	4.7	PP-OCRv4 mobile-side text detection model with higher efficiency, suitable for deployment on edge devices

Text Recognition Module:

Model	Model Download Links	Recognition Avg Accuracy(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (MB)	Introduction
PP-OCRv5_server_rec	Inference Model/Pretrained Model	86.38	8.46 / 2.36	31.21 / 31.21	81	PP-OCRv5_rec is a next-generation text recognition model. It aims to efficiently and accurately support the recognition of four major languages—Simplified Chinese, Traditional Chinese, English, and Japanese—as well as complex text scenarios such as handwriting, vertical text, pinyin, and rare characters using a single model. While maintaining recognition performance, it balances inference speed and model robustness, providing efficient and accurate technical support for document understanding in various scenarios.
PP-OCRv5_mobile_rec	Inference Model/Pretrained Model	81.29	5.43 / 1.46	21.20 / 5.32	16
PP-OCRv4_server_rec_doc	Inference Model/Pretrained Model	86.58	8.69 / 2.78	37.93 / 37.93	182	PP-OCRv4_server_rec_doc is trained on a mixed dataset of more Chinese document data and PP-OCR training data, building upon PP-OCRv4_server_rec. It enhances the recognition capabilities for some Traditional Chinese characters, Japanese characters, and special symbols, supporting over 15,000 characters. In addition to improving document-related text recognition, it also enhances general text recognition capabilities.
PP-OCRv4_mobile_rec	Inference Model/Pretrained Model	78.74	5.26 / 1.12	17.48 / 3.61	10.5	A lightweight recognition model of PP-OCRv4 with high inference efficiency, suitable for deployment on various hardware devices, including edge devices.
PP-OCRv4_server_rec	Inference Model/Pretrained Model	85.19	8.75 / 2.49	36.93 / 36.93	173	The server-side model of PP-OCRv4, offering high inference accuracy and deployable on various servers.
en_PP-OCRv4_mobile_rec	Inference Model/Pretrained Model	70.39	4.81 / 1.23	17.20 / 4.18	7.5	An ultra-lightweight English recognition model trained based on the PP-OCRv4 recognition model, supporting English and numeric character recognition.

> ❗ The above section lists the **6 core models** that are primarily supported by the text recognition module. In total, the module supports **20 comprehensive models**, including multiple multilingual text recognition models. Below is the complete list of models:

👉Details of the Model List

* PP-OCRv5 Multi-Scenario Models

Model	Model Download Links	Avg Accuracy for Chinese Recognition (%)	Avg Accuracy for English Recognition (%)	Avg Accuracy for Traditional Chinese Recognition (%)	Avg Accuracy for Japanese Recognition (%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (MB)	Introduction
PP-OCRv5_server_rec	Inference Model/Pretrained Model	86.38	64.70	93.29	60.35	8.46 / 2.36	31.21 / 31.21	81	PP-OCRv5_rec is a next-generation text recognition model. It aims to efficiently and accurately support the recognition of four major languages—Simplified Chinese, Traditional Chinese, English, and Japanese—as well as complex text scenarios such as handwriting, vertical text, pinyin, and rare characters using a single model. While maintaining recognition performance, it balances inference speed and model robustness, providing efficient and accurate technical support for document understanding in various scenarios.
PP-OCRv5_mobile_rec	Inference Model/Pretrained Model	81.29	66.00	83.55	54.65	5.43 / 1.46	21.20 / 5.32	16

* Chinese Recognition Models

Model	Model Download Link	Recognition Avg Accuracy (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PP-OCRv4_server_rec_doc	Inference Model/Training Model	86.58	8.69 / 2.78	37.93 / 37.93	182	PP-OCRv4_server_rec_doc is based on PP-OCRv4_server_rec, trained with a mix of more Chinese document data and PP-OCR training data, increasing the recognition capabilities for some Traditional Chinese, Japanese, and special characters, supporting recognition of over 15,000 characters. In addition to improving the document-related text recognition capabilities, it also enhances general text recognition capabilities.
PP-OCRv4_mobile_rec	Inference Model/Training Model	78.74	5.26 / 1.12	17.48 / 3.61	10.5	PP-OCRv4's lightweight recognition model has high inference efficiency and can be deployed on various hardware, including edge devices.
PP-OCRv4_server_rec	Inference Model/Training Model	85.19	8.75 / 2.49	36.93 / 36.93	173	PP-OCRv4's server-side model has high inference accuracy and can be deployed on various servers.
PP-OCRv3_mobile_rec	Inference Model/Training Model	72.96	3.89 / 1.16	8.72 / 3.56	10.3	PP-OCRv3's lightweight recognition model has high inference efficiency and can be deployed on various hardware, including edge devices.

Model	Model Download Link	Recognition Avg Accuracy (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
ch_SVTRv2_rec	Inference Model/Training Model	68.81	10.38 / 8.31	66.52 / 30.83	80.5	SVTRv2 is a server-side text recognition model developed by the OpenOCR team at Fudan University's Vision and Learning Laboratory (FVL). It won first place in the PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task, achieving a 6% improvement in end-to-end recognition accuracy compared to PP-OCRv4.

Model	Model Download Link	Recognition Avg Accuracy (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
ch_RepSVTR_rec	Inference Model/Training Model	65.07	6.29 / 1.57	20.64 / 5.40	48.8	RepSVTR is a mobile text recognition model based on SVTRv2, which won first place in the PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task, achieving a 2.5% improvement in end-to-end recognition accuracy compared to PP-OCRv4, while maintaining the same inference speed.

* English Recognition Models

Model	Model Download Link	Recognition Avg Accuracy (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
en_PP-OCRv4_mobile_rec	Inference Model/Training Model	70.39	4.81 / 1.23	17.20 / 4.18	7.5	This ultra-lightweight English recognition model is trained based on the PP-OCRv4 recognition model, supporting English and digit recognition.
en_PP-OCRv3_mobile_rec	Inference Model/Training Model	70.69	3.56 / 0.78	8.44 / 5.78	17.3	This ultra-lightweight English recognition model is trained based on the PP-OCRv3 recognition model, supporting English and digit recognition.

* Multilingual Recognition Models

Model	Model Download Link	Recognition Avg Accuracy (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
korean_PP-OCRv3_mobile_rec	Inference Model/Training Model	60.21	3.73 / 0.98	8.76 / 2.91	9.6	This ultra-lightweight Korean recognition model is trained based on the PP-OCRv3 recognition model, supporting Korean and digit recognition.
japan_PP-OCRv3_mobile_rec	Inference Model/Training Model	45.69	3.86 / 1.01	8.62 / 2.92	9.8	This ultra-lightweight Japanese recognition model is trained based on the PP-OCRv3 recognition model, supporting Japanese and digit recognition.
chinese_cht_PP-OCRv3_mobile_rec	Inference Model/Training Model	82.06	3.90 / 1.16	9.24 / 3.18	10.8	This ultra-lightweight Traditional Chinese recognition model is trained based on the PP-OCRv3 recognition model, supporting Traditional Chinese and digit recognition.
te_PP-OCRv3_mobile_rec	Inference Model/Training Model	95.88	3.59 / 0.81	8.28 / 6.21	85 M	This ultra-lightweight Telugu recognition model is trained based on the PP-OCRv3 recognition model, supporting Telugu and digit recognition.
ka_PP-OCRv3_mobile_rec	Inference Model/Training Model	96.96	3.49 / 0.89	8.63 / 2.77	17.4	This ultra-lightweight Kannada recognition model is trained based on the PP-OCRv3 recognition model, supporting Kannada and digit recognition.
ta_PP-OCRv3_mobile_rec	Inference Model/Training Model	76.83	3.49 / 0.86	8.35 / 3.41	8.7	This ultra-lightweight Tamil recognition model is trained based on the PP-OCRv3 recognition model, supporting Tamil and digit recognition.
latin_PP-OCRv3_mobile_rec	Inference Model/Training Model	76.93	3.53 / 0.78	8.50 / 6.83	8.7	This ultra-lightweight Latin recognition model is trained based on the PP-OCRv3 recognition model, supporting Latin and digit recognition.
arabic_PP-OCRv3_mobile_rec	Inference Model/Training Model	73.55	3.60 / 0.83	8.44 / 4.69	17.3	This ultra-lightweight Arabic alphabet recognition model is trained based on the PP-OCRv3 recognition model, supporting Arabic letters and digit recognition.
cyrillic_PP-OCRv3_mobile_rec	Inference Model/Training Model	94.28	3.56 / 0.79	8.22 / 2.76	8.7	This ultra-lightweight Slavic alphabet recognition model is trained based on the PP-OCRv3 recognition model, supporting Slavic letters and digit recognition.
devanagari_PP-OCRv3_mobile_rec	Inference Model/Training Model	96.44	3.60 / 0.78	6.95 / 2.87	8.7	This ultra-lightweight Devanagari alphabet recognition model is trained based on the PP-OCRv3 recognition model, supporting Devanagari letters and digit recognition.

Layout Region Detection Module Models:

Model	Model Download Link	mAP(0.5) (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PP-DocLayout_plus-L	Inference Model/Training Model	83.2	53.03 / 17.23	634.62 / 378.32	126.01	This higher-precision layout region localization model is trained based on RT-DETR-L on a self-built dataset that includes Chinese and English papers, multi-column magazines, newspapers, PPTs, contracts, books, exam papers, research reports, ancient texts, Japanese documents, and vertical text documents.
PP-DocLayout-L	Inference Model/Training Model	90.4	33.59 / 33.59	503.01 / 251.08	123.76	This high-precision layout region localization model is trained based on RT-DETR-L on a self-built dataset that includes Chinese and English papers, magazines, contracts, books, exam papers, and research reports.
PP-DocLayout-M	Inference Model/Training Model	75.2	13.03 / 4.72	43.39 / 24.44	22.578	This layout region localization model balances accuracy and efficiency, trained based on PicoDet-L on a self-built dataset that includes Chinese and English papers, magazines, contracts, books, exam papers, and research reports.
PP-DocLayout-S	Inference Model/Training Model	70.9	11.54 / 3.86	18.53 / 6.29	4.834	This highly efficient layout region localization model is trained based on PicoDet-S on a self-built dataset that includes Chinese and English papers, magazines, contracts, books, exam papers, and research reports.

> ❗ The above lists the 4 core models that are key to the layout detection module. The module supports a total of 12 complete models, including multiple pre-defined models for different categories. The complete model list is as follows:

👉 Model List Details

* Table Layout Detection Models

Model	Model Download Link	mAP(0.5) (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PicoDet_layout_1x_table	Inference Model/Training Model	97.5	9.57 / 6.63	27.66 / 16.75	7.4	This high-efficiency layout region localization model is trained based on PicoDet-1x on a self-built dataset, capable of locating tables as one type of region.

* 3-Class Layout Detection Models, Including Tables, Images, and Stamps

Model	Model Download Link	mAP(0.5) (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PicoDet-S_layout_3cls	Inference Model/Training Model	88.2	8.43 / 3.44	17.60 / 6.51	4.8	This high-efficiency layout region localization model is trained using a self-built dataset of Chinese and English papers, magazines, and research reports based on the lightweight model PicoDet-S.
PicoDet-L_layout_3cls	Inference Model/Training Model	89.0	12.80 / 9.57	45.04 / 23.86	22.6	This layout region localization model balances efficiency and accuracy, trained using a self-built dataset of Chinese and English papers, magazines, and research reports based on the model PicoDet-L.
RT-DETR-H_layout_3cls	Inference Model/Training Model	95.8	114.80 / 25.65	924.38 / 924.38	470.1	This high-precision layout region localization model is trained using a self-built dataset of Chinese and English papers, magazines, and research reports based on the model RT-DETR-H.

* 5-Class English Document Region Detection Models, Including Text, Titles, Tables, Images, and Lists

Model	Model Download Link	mAP(0.5) (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PicoDet_layout_1x	Inference Model/Training Model	97.8	9.62 / 6.75	26.96 / 12.77	7.4	This high-efficiency English document layout region localization model is trained on the PubLayNet dataset.

* 17-Class Region Detection Models, Including 17 Common Layout Categories: Title, Image, Text, Number, Abstract, Content, Chart Title, Formula, Table, Table Title, References, Document Title, Footnote, Header, Algorithm, Footer, and Stamp

Model	Model Download Link	mAP(0.5) (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PicoDet-S_layout_17cls	Inference Model/Training Model	87.4	8.80 / 3.62	17.51 / 6.35	4.8	This high-efficiency layout region localization model is trained using a self-built dataset of Chinese and English papers, magazines, and research reports based on the lightweight model PicoDet-S.
PicoDet-L_layout_17cls	Inference Model/Training Model	89.0	12.60 / 10.27	43.70 / 24.42	22.6	This layout region localization model balances efficiency and accuracy, trained using a self-built dataset of Chinese and English papers, magazines, and research reports based on the model PicoDet-L.
RT-DETR-H_layout_17cls	Inference Model/Training Model	98.3	115.29 / 101.18	964.75 / 964.75	470.2	This high-precision layout region localization model is trained using a self-built dataset of Chinese and English papers, magazines, and research reports based on the model RT-DETR-H.

Text Image Unwarping Module Models (Optional):

Model	Model Download Link	CER	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms) [Normal Mode / High-Performance Mode]	Model Storage Size (MB)	Description
UVDoc	Inference Model/Training Model	0.179	19.05 / 19.05	- / 869.82	30.3	High-precision text image correction model.

Document Image Orientation Classification Module Models (Optional):

Model	Model Download Link	Top-1 Acc (%)	GPU Inference Time (ms) [Regular Mode / High-Performance Mode]	CPU Inference Time (ms) [Regular Mode / High-Performance Mode]	Model Storage Size (MB)	Description
PP-LCNet_x1_0_doc_ori	Inference Model/Training Model	99.06	2.62 / 0.59	3.24 / 1.19	7	Based on PP-LCNet_x1_0, this document image classification model includes four categories: 0 degrees, 90 degrees, 180 degrees, and 270 degrees.

Testing Environment Information:

Performance Testing Environment
- Test Dataset:
  - Document Image Orientation Classification Model: A self-built dataset by PaddleX, covering multiple scenarios including identification documents and documents, containing 1,000 images.
  - Layout Region Detection Model: A self-built layout region detection dataset by PaddleOCR, containing 500 common document type images, including Chinese and English papers, magazines, contracts, books, exam papers, and research reports.
  - Table Layout Detection Model: A self-built layout table region detection dataset by PaddleOCR, containing 7,835 images with tables in Chinese and English papers.
  - 3-Class Layout Detection Model: A self-built layout region detection dataset by PaddleOCR, containing 1,154 common document type images including Chinese and English papers, magazines, and research reports.
  - 5-Class English Document Region Detection Model: The evaluation dataset from PubLayNet, containing 11,245 images of English documents.
  - 17-Class Region Detection Model: A self-built layout region detection dataset by PaddleOCR, containing 892 common document type images including Chinese and English papers, magazines, and research reports.
  - Table Structure Recognition Model: A high-difficulty Chinese table recognition dataset built internally by PaddleX.
  - Table Cell Detection Model: An evaluation set built internally by PaddleX.
  - Table Classification Model: An evaluation set built internally by PaddleX.
  - Text Detection Model: A Chinese dataset built by PaddleOCR, covering multiple scenarios including street scenes, web images, documents, and handwriting, with 500 images for detection.
  - Chinese Recognition Model: A Chinese dataset built by PaddleOCR, covering multiple scenarios including street scenes, web images, documents, and handwriting, with 11,000 images for text recognition.
  - ch_SVTRv2_rec: The evaluation set from PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task A-list.
  - ch_RepSVTR_rec: The evaluation set from PaddleOCR Algorithm Model Challenge - Task 1: OCR End-to-End Recognition Task B-list.
  - English Recognition Model: An English dataset built internally by PaddleX.
  - Multilingual Recognition Model: A multilingual dataset built internally by PaddleX.
- Hardware Configuration:
  - GPU: NVIDIA Tesla T4
  - CPU: Intel Xeon Gold 6271C @ 2.60GHz
- Software Environment:
  - Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6
  - paddlepaddle 3.0.0 / paddleocr 3.0.3
Inference Mode Information

Mode	GPU Configuration	CPU Configuration	Acceleration Technology Combination
Regular Mode	FP32 Precision / No TRT Acceleration	FP32 Precision / 8 Threads	PaddleInference
High-Performance Mode	Optimal combination of prior precision type and acceleration strategy	FP32 Precision / 8 Threads	Optimal backend (Paddle/OpenVINO/TRT, etc.) selected based on prior knowledge

If you prioritize model accuracy, please choose models with higher accuracy; if you care more about inference speed, please select models with faster inference speeds; if you focus on model storage size, please choose models with smaller storage volumes. ## 2. Quick Start Before using the table structure recognition V2 pipeline locally, please ensure that you have completed the installation of the wheel package according to the [installation guide](../installation.md). If you prefer to install dependencies selectively, please refer to the relevant instructions in the installation documentation. The corresponding dependency group for this pipeline is doc-parser. After installation, you can experience it locally using the command line or Python integration. Please note: If you encounter issues such as the program becoming unresponsive, unexpected program termination, running out of memory resources, or extremely slow inference during execution, please try adjusting the configuration according to the documentation, such as disabling unnecessary features or using lighter-weight models. ### 2.1 Command Line Experience A single command allows you to quickly experience the effects of the table_recognition_v2 pipeline: ```bash paddleocr table_recognition_v2 -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition_v2.jpg # Specify whether to use the document orientation classification model with --use_doc_orientation_classify paddleocr table_recognition_v2 -i ./table_recognition_v2.jpg --use_doc_orientation_classify True # Specify whether to use the text image unwarping module with --use_doc_unwarping paddleocr table_recognition_v2 -i ./table_recognition_v2.jpg --use_doc_unwarping True # Specify the device to use GPU for model inference with --device paddleocr table_recognition_v2 -i ./table_recognition_v2.jpg --device gpu ```

More command line parameters are supported. Click to expand for detailed descriptions of the command line parameters

Parameter	Description	Type	Default Value
`input`	Meaning:Data to be predicted, required. Description: Local path to image files or PDF files: `/root/data/img.jpg`; as URL links, such as network URLs for image files or PDF files: example; as local directories, the directory must contain images to be predicted, such as local path: `/root/data/` (currently, predictions do not support directories that contain PDF files; the PDF file must be specified to the specific file path).	`str`
`save_path`	Meaning:Specify the path to save the inference result file. Description: If not set, the inference result will not be saved locally.	`str`
`layout_detection_model_name`	Meaning:Name of the layout detection model. Description: If not set, the default model of the pipeline will be used.	`str`
`layout_detection_model_dir`	Directory path of the layout detection model. If not set, the official model will be downloaded.	`str`
`table_classification_model_name`	Meaning:Name of the table classification model. Description: If not set, the default model of the pipeline will be used.	`str`
`table_classification_model_dir`	Meaning:Directory path of the table classification model. Description: If not set, the official model will be downloaded.	`str`
`wired_table_structure_recognition_model_name`	Meaning:Name of the wired table structure recognition model. Description: If not set, the default model of the pipeline will be used.	`str`
`wired_table_structure_recognition_model_dir`	Meaning:Directory path of the wired table structure recognition model. Description: If not set, the official model will be downloaded.	`str`
`wireless_table_structure_recognition_model_name`	Meaning:Name of the wireless table structure recognition model. Description: If not set, the default model of the pipeline will be used.	`str`
`wireless_table_structure_recognition_model_dir`	Meaning:Directory path of the wireless table structure recognition model. Description: If not set, the official model will be downloaded.	`str`
`wired_table_cells_detection_model_name`	Meaning:Name of the wired table cell detection model. Description: If not set, the default model of the pipeline will be used.	`str`
`wired_table_cells_detection_model_dir`	Meaning:Directory path of the wired table cell detection model. Description: If not set, the official model will be downloaded.	`str`
`wireless_table_cells_detection_model_name`	Meaning:Name of the wireless table cell detection model. Description: If not set, the default model of the pipeline will be used.	`str`
`wireless_table_cells_detection_model_dir`	Meaning:Directory path of the wireless table cell detection model. Description: If not set, the official model will be downloaded.	`str`
`doc_orientation_classify_model_name`	Meaning:Name of the document orientation classification model. Description: If not set, the default model of the pipeline will be used.	`str`
`doc_orientation_classify_model_dir`	Meaning:Directory path of the document orientation classification model. Description: If not set, the official model will be downloaded.	`str`
`doc_unwarping_model_name`	Meaning:Name of the text image unwarping model. Description: If not set, the default model of the pipeline will be used.	`str`
`doc_unwarping_model_dir`	Meaning:Directory path of the text image unwarping model. Description: If not set, the official model will be downloaded.	`str`
`text_detection_model_name`	Meaning:Name of the text detection model. Description: If not set, the default model of the pipeline will be used.	`str`
`text_detection_model_dir`	Meaning:Directory path of the text detection model. Description: If not set, the official model will be downloaded.	`str`
`text_det_limit_side_len`	Meaning:Image side length limit for text detection. Description: Any integer greater than `0`. If not set, the value initialized by the pipeline will be used, which defaults to `960`.	`int`
`text_det_limit_type`	Meaning:Type of the image side length limit for text detection. Description: Supports `min` and `max`. `min` ensures that the shortest side of the image is not less than `det_limit_side_len`, while `max` ensures that the longest side of the image is not greater than `limit_side_len`. If not set, the value initialized by the pipeline will be used, which defaults to `max`.	`str`
`text_det_thresh`	Meaning:Detection pixel threshold. In the output probability map, only pixels with a score greater than this threshold will be considered text pixels. Description: Any floating-point number greater than `0`. If not set, the value initialized by the pipeline will be used, which defaults to `0.3`.	`float`
`text_det_box_thresh`	Meaning:Detection box threshold. When the average score of all pixels within the detection result box is greater than this threshold, the result is considered a text area. Description: Any floating-point number greater than `0`. If not set, the value initialized by the pipeline will be used, which defaults to `0.6`.	`float`
`text_det_unclip_ratio`	Meaning:Text detection expansion coefficient. This method expands the text area; the larger this value, the larger the expanded area. Description: Any floating-point number greater than `0`. If not set, the value initialized by the pipeline will be used, which defaults to `2.0`.	`float`
`text_recognition_model_name`	Meaning:Name of the text recognition model. Description: If not set, the default model of the pipeline will be used.	`str`
`text_recognition_model_dir`	Meaning:Directory path of the text recognition model. Description: If not set, the official model will be downloaded.	`str`
`text_recognition_batch_size`	Meaning:Batch size for the text recognition model. Description: If not set, the default batch size will be set to `1`.	`int`
`text_rec_score_thresh`	Meaning:Text recognition threshold. Text results with a score greater than this threshold will be retained. Description: Any floating-point number greater than `0`. If not set, the value initialized by the pipeline will be used, which defaults to `0.0`. That is, no threshold is set.	`float`
`use_doc_orientation_classify`	Meaning:Whether to load and use the document orientation classification module. Description: If not set, the value initialized by the pipeline will be used, which defaults to `True`.	`bool`
`use_doc_unwarping`	Meaning:Whether to load and use the text image unwarping module. Description: If not set, the value initialized by the pipeline will be used, which defaults to `True`.	`bool`
`use_layout_detection`	Meaning:Whether to load and use the layout detection module. Description: If not set, the value initialized by the pipeline will be used, which defaults to `True`.	`bool`
`use_ocr_model`	Meaning:Whether to load and use the OCR module. Description: If not set, the value initialized by the pipeline will be used, which defaults to `True`.	`bool`
`device`	Meaning:The device used for inference. Description: Supports specifying a specific card number: CPU: For example, `cpu` indicates using CPU for inference; GPU: For example, `gpu:0` indicates using the first GPU for inference; NPU: For example, `npu:0` indicates using the first NPU for inference; XPU: For example, `xpu:0` indicates using the first XPU for inference; MLU: For example, `mlu:0` indicates using the first MLU for inference; DCU: For example, `dcu:0` indicates using the first DCU for inference; MetaX GPU: For example, `metax_gpu:0` indicates using the first MetaX GPU for inference; Iluvatar GPU: For example, `iluvatar_gpu:0` indicates using the first Iluvatar GPU for inference; If not set, the pipeline initialized value for this parameter will be used. During initialization, the local GPU device 0 will be preferred; if unavailable, the CPU device will be used.	`str`
`enable_hpi`	Meaning:Whether to enable high-performance inference.	`bool`	`False`
`use_tensorrt`	Meaning:Whether to use the Paddle Inference TensorRT subgraph engine. Description: If the model does not support acceleration through TensorRT, setting this flag will not enable acceleration. For Paddle with CUDA version 11.8, the compatible TensorRT version is 8.x (x>=6), and it is recommended to install TensorRT 8.6.1.6.	`bool`	`False`
`precision`	Meaning:Computation precision, such as fp32, fp16.	`str`	`fp32`
`enable_mkldnn`	Meaning:Whether to enable MKL-DNN acceleration for inference. Description: If MKL-DNN is unavailable or the model does not support it, acceleration will not be used even if this flag is set.	`bool`	`True`
`mkldnn_cache_capacity`	Meaning:MKL-DNN cache capacity.	`int`	`10`
`cpu_threads`	Meaning:Number of threads to use for inference on the CPU.	`int`	`8`
`paddlex_config`	Meaning:Path to PaddleX pipeline configuration file.	`str`

To run inference on the [example image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition_v2.jpg), you can use the following command: ```bash paddleocr table_recognition_v2 -i ./table_recognition_v2.jpg --use_doc_orientation_classify False --use_doc_unwarping False ``` The running results will be printed to the terminal. The default configuration of the table_recognition_v2 pipeline's running results is as follows: ``` {'res': {'input_path': 'table_recognition_v2.jpg', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_layout_detection': True, 'use_ocr_model': True}, 'layout_det_res': {'input_path': None, 'page_index': None, 'boxes': [{'cls_id': 8, 'label': 'table', 'score': 0.86655592918396, 'coordinate': [0.0125130415, 0.41920784, 1281.3737, 585.3884]}]}, 'overall_ocr_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'dt_polys': array([[[ 9, 21], ..., [ 9, 59]], ..., [[1046, 536], ..., [1046, 573]]], dtype=int16), 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0, 'rec_texts': ['部门', '报销人', '报销事由', '批准人：', '单据', '张', '合计金额', '元', '车费票', '其', '火车费票', '飞机票', '中', '旅住宿费', '其他', '补贴'], 'rec_scores': array([0.99958128, ..., 0.99317062]), 'rec_polys': array([[[ 9, 21], ..., [ 9, 59]], ..., [[1046, 536], ..., [1046, 573]]], dtype=int16), 'rec_boxes': array([[ 9, ..., 59], ..., [1046, ..., 573]], dtype=int16)}, 'table_res_list': [{'cell_box_list': [array([ 0.13052222, ..., 73.08310249]), array([104.43082511, ..., 73.27777413]), array([319.39041221, ..., 73.30439308]), array([424.2436837 , ..., 73.44736794]), array([580.75836265, ..., 73.24003914]), array([723.04370201, ..., 73.22717598]), array([984.67315757, ..., 73.20420387]), array([1.25130415e-02, ..., 5.85419208e+02]), array([984.37072837, ..., 137.02281502]), array([984.26586998, ..., 201.22290352]), array([984.24017417, ..., 585.30775765]), array([1039.90606773, ..., 265.44664314]), array([1039.69549644, ..., 329.30540779]), array([1039.66546714, ..., 393.57319954]), array([1039.5122689 , ..., 457.74644783]), array([1039.55535972, ..., 521.73030403]), array([1039.58612144, ..., 585.09468392])], 'pred_html': '

部门	报销人	报销事由	批准人：
			单据张
			合计金额元
			其中	车费票
				火车费票
				飞机票
				旅住宿费
				其他
				补贴

', 'table_ocr_pred': {'rec_polys': array([[[ 9, 21], ..., [ 9, 59]], ..., [[1046, 536], ..., [1046, 573]]], dtype=int16), 'rec_texts': ['部门', '报销人', '报销事由', '批准人：', '单据', '张', '合计金额', '元', '车费票', '其', '火车费票', '飞机票', '中', '旅住宿费', '其他', '补贴'], 'rec_scores': array([0.99958128, ..., 0.99317062]), 'rec_boxes': array([[ 9, ..., 59], ..., [1046, ..., 573]], dtype=int16)}}]}} ``` The visualization results are saved under save_path, and the visualization results are as follows:

### 2.2 Python Script Integration The command line method is designed for quick experience and viewing effects. Generally, in a project, it is often necessary to integrate through code. You can complete quick inference of the pipeline with just a few lines of code. The inference code is as follows: ```python from paddleocr import TableRecognitionPipelineV2 pipeline = TableRecognitionPipelineV2() # ocr = TableRecognitionPipelineV2(use_doc_orientation_classify=True) # Specify whether to use the document orientation classification model with use_doc_orientation_classify # ocr = TableRecognitionPipelineV2(use_doc_unwarping=True) # Specify whether to use the text image unwarping module with use_doc_unwarping # ocr = TableRecognitionPipelineV2(device="gpu") # Specify the device to use GPU for model inference output = pipeline.predict("./table_recognition_v2.jpg") for res in output: res.print() ## Print the predicted structured output res.save_to_img("./output/") res.save_to_xlsx("./output/") res.save_to_html("./output/") res.save_to_json("./output/") ``` In the above Python script, the following steps are performed: (1) Instantiate the general table recognition V2 pipeline object using TableRecognitionPipelineV2(). The specific parameter descriptions are as follows:

Parameter	Description	Type	Default Value
`layout_detection_model_name`	Meaning:Name of the layout detection model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`layout_detection_model_dir`	Meaning:Directory path of the layout detection model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`table_classification_model_name`	Meaning:Name of the table classification model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`table_classification_model_dir`	Meaning:Directory path of the table classification model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`wired_table_structure_recognition_model_name`	Meaning:Name of the wired table structure recognition model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`wired_table_structure_recognition_model_dir`	Meaning:Directory path of the wired table structure recognition model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`wireless_table_structure_recognition_model_name`	Meaning:Name of the wireless table structure recognition model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`wireless_table_structure_recognition_model_dir`	Meaning:Directory path of the wireless table structure recognition model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`wired_table_cells_detection_model_name`	Name of the wired table cell detection model.	Meaning:Name of the wired table cell detection model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`wired_table_cells_detection_model_dir`	Directory path of the wired table cell detection model.	Meaning:Directory path of the wired table cell detection model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`wireless_table_cells_detection_model_name`	Name of the wireless table cell detection model.	Meaning:Name of the wireless table cell detection model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`wireless_table_cells_detection_model_dir`	Directory path of the wireless table cell detection model.	Meaning:Directory path of the wireless table cell detection model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`doc_orientation_classify_model_name`	Name of the document orientation classification model.	Meaning:Name of the document orientation classification model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`doc_orientation_classify_model_dir`	Directory path of the document orientation classification model.	Meaning:Directory path of the document orientation classification model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`doc_unwarping_model_name`	Name of the text image unwarping model.	Meaning:Name of the text image unwarping model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`doc_unwarping_model_dir`	Directory path of the text image unwarping model.	Meaning:Directory path of the text image unwarping model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`text_detection_model_name`	Name of the text detection model.	Meaning:Name of the text detection model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`text_detection_model_dir`	Directory path of the text detection model.	Meaning:Directory path of the text detection model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`text_det_limit_side_len`	Meaning:Image side length limit for text detection. Description: int: Any integer greater than `0`; None: If set to `None`, the value initialized by the pipeline will be used, which defaults to `960`.	`int\|None`	`None`
`text_det_limit_type`	Meaning:Type of the image side length limit for text detection. Description: str: Supports `min` and `max`. `min` ensures that the shortest side of the image is not less than `det_limit_side_len`, while `max` ensures that the longest side of the image is not greater than `limit_side_len`; None: If set to `None`, the value initialized by the pipeline will be used, which defaults to `max`.	`str\|None`	`None`
`text_det_thresh`	Meaning:Detection pixel threshold. In the output probability map, only pixels with a score greater than this threshold will be considered text pixels. Description: float: Any floating-point number greater than `0`; None: If set to `None`, the value initialized by the pipeline will be used, which defaults to `0.3`.	`float\|None`	`None`
`text_det_box_thresh`	Meaning:Detection box threshold. When the average score of all pixels within the detection result box is greater than this threshold, the result is considered a text area. Description: float: Any floating-point number greater than `0`; None: If set to `None`, the value initialized by the pipeline will be used, which defaults to `0.6`.	`float\|None`	`None`
`text_det_unclip_ratio`	Meaning:Text detection expansion coefficient. This method expands the text area; the larger this value, the larger the expanded area. Description: float: Any floating-point number greater than `0`; None: If set to `None`, the value initialized by the pipeline will be used, which defaults to `2.0`.	`float\|None`	`None`
`text_recognition_model_name`	Meaning:Name of the text recognition model. Description: If set to `None`, the default model of the pipeline will be used.	`str\|None`	`None`
`text_recognition_model_dir`	Directory path of the text recognition model.	Meaning:Directory path of the text recognition model. Description: If set to `None`, the official model will be downloaded.	`str\|None`	`None`
`text_recognition_batch_size`	Batch size for the text recognition model.	Meaning:Batch size for the text recognition model. Description: If set to `None`, the default batch size will be set to `1`.	`int\|None`	`None`
`text_rec_score_thresh`	Meaning:Text recognition threshold. Text results with a score greater than this threshold will be retained. Description: float: Any floating-point number greater than `0`; None: If set to `None`, the value initialized by the pipeline will be used, which defaults to `0.0`. That is, no threshold is set.	`float\|None`	`None`
`use_doc_orientation_classify`	Meaning:Whether to load and use the document orientation classification module. Description: If set to `None`, the value initialized by the pipeline will be used, which defaults to `True`.	`bool\|None`	`None`
`use_doc_unwarping`	Whether to load and use the text image unwarping module.	Meaning:Whether to load and use the text image unwarping module. Description: If set to `None`, the value initialized by the pipeline will be used, which defaults to `True`.	`bool\|None`	`None`
`use_layout_detection`	Whether to load and use the layout detection module.	Meaning:Whether to load and use the layout detection module. Description: If set to `None`, the value initialized by the pipeline will be used, which defaults to `True`.	`bool\|None`	`None`
`use_ocr_model`	Whether to load and use the OCR module.	Meaning:Whether to load and use the OCR module. Description: If set to `None`, the value initialized by the pipeline will be used, which defaults to `True`.	`bool\|None`	`None`
`device`	Meaning:The device used for inference. Description: Supports specifying a specific card number: CPU: For example, `cpu` indicates using CPU for inference; GPU: For example, `gpu:0` indicates using the first GPU for inference; NPU: For example, `npu:0` indicates using the first NPU for inference; XPU: For example, `xpu:0` indicates using the first XPU for inference; MLU: For example, `mlu:0` indicates using the first MLU for inference; DCU: For example, `dcu:0` indicates using the first DCU for inference; MetaX GPU: For example, `metax_gpu:0` indicates using the first MetaX GPU for inference; Iluvatar GPU: For example, `iluvatar_gpu:0` indicates using the first Iluvatar GPU for inference; None: If set to `None`, the pipeline initialized value for this parameter will be used. During initialization, the local GPU device 0 will be preferred; if unavailable, the CPU device will be used.	`str\|None`	`None`
`enable_hpi`	Meaning:Whether to enable high-performance inference.	`bool`	`False`
`use_tensorrt`	Meaning:Whether to use the Paddle Inference TensorRT subgraph engine. If the model does not support acceleration through TensorRT, setting this flag will not enable acceleration. Description: For Paddle with CUDA version 11.8, the compatible TensorRT version is 8.x (x>=6), and it is recommended to install TensorRT 8.6.1.6.	`bool`	`False`
`precision`	Meaning:Computation precision, such as fp32, fp16.	`str`	`"fp32"`
`enable_mkldnn`	Meaning:Whether to enable MKL-DNN acceleration for inference. Description: If MKL-DNN is unavailable or the model does not support it, acceleration will not be used even if this flag is set.	`bool`	`True`
`mkldnn_cache_capacity`	Meaning:MKL-DNN cache capacity.	`int`	`10`
`cpu_threads`	Meaning:Number of threads to use for inference on the CPU.	`int`	`8`
`paddlex_config`	Meaning:Path to PaddleX pipeline configuration file.	`str\|None`	`None`

(2) Call the predict() method of the general table recognition V2 pipeline object to perform inference prediction, which returns a result list. Additionally, the pipeline also provides the predict_iter() method. Both methods accept the same parameters and return results in the same way; the difference is that predict_iter() returns a generator, allowing for gradual processing and retrieval of prediction results, suitable for handling large datasets or for scenarios where memory savings are desired. You can choose to use either method based on your actual needs. The parameters and descriptions of the predict() method are as follows:

Parameter	Description	Type	Default Value
`input`	Meaning:Data to be predicted, supports multiple input types, required. Description: Python Var: For example, image data represented as `numpy.ndarray`; str: Local path to image files or PDF files: `/root/data/img.jpg`; as URL links, such as network URLs for image files or PDF files: example; as local directories, the directory must contain images to be predicted, such as local path: `/root/data/` (currently, predictions do not support directories that contain PDF files; the PDF file must be specified to the specific file path); list: The elements of the list must be of the above types, such as `[numpy.ndarray, numpy.ndarray]`, `["/root/data/img1.jpg", "/root/data/img2.jpg"]`, `["/root/data1", "/root/data2"]`.	`Python Var\|str\|list`
`use_doc_orientation_classify`	Meaning:Whether to use the document orientation classification module during inference.	`bool\|None`	`None`
`use_doc_unwarping`	Meaning:Whether to use the text image unwarping module during inference.	`bool\|None`	`None`
`use_layout_detection`	Meaning:Whether to use the layout detection module during inference.	`bool\|None`	`None`
`use_ocr_model`	Meaning:Whether to use the `ocr` model during inference.	`bool\|None`	`None`
`text_det_limit_side_len`	Meaning:Same meaning as the instantiation parameters. Description: If set to `None`, the instantiation value is used; otherwise, this parameter takes precedence.	`int\|None`	`None`
`text_det_limit_type`	Meaning:Same meaning as the instantiation parameters. Description: If set to `None`, the instantiation value is used; otherwise, this parameter takes precedence.	`str\|None`	`None`
`text_det_thresh`	Meaning:Same meaning as the instantiation parameters. Description: If set to `None`, the instantiation value is used; otherwise, this parameter takes precedence.	`float\|None`	`None`
`text_det_box_thresh`	Meaning:Same meaning as the instantiation parameters. Description: If set to `None`, the instantiation value is used; otherwise, this parameter takes precedence.	`float\|None`	`None`
`text_det_unclip_ratio`	Meaning:Same meaning as the instantiation parameters. Description: If set to `None`, the instantiation value is used; otherwise, this parameter takes precedence.	`float\|None`	`None`
`text_rec_score_thresh`	Meaning:Same meaning as the instantiation parameters. Description: If set to `None`, the instantiation value is used; otherwise, this parameter takes precedence.	`float\|None`	`None`
`use_e2e_wired_table_rec_model`	Meaning:Whether to use the wired end-to-end table recognition mode during inference.	`bool`	`False`
`use_e2e_wireless_table_rec_model`	Meaning:Whether to use the wireless end-to-end table recognition mode during inference.	`bool`	`False`
`use_wired_table_cells_trans_to_html`	Meaning:Whether to use the wired table cell detection result direct-to-HTML mode during inference. Description: If enabled, it directly constructs the HTML based on the geometric relationships of the wired table cell detection results.	`bool`	`False`
`use_wireless_table_cells_trans_to_html`	Meaning:Whether to use the wireless table cell detection result direct-to-HTML mode during inference. Description: If enabled, it directly constructs the HTML based on the geometric relationships of the wireless table cell detection results.	`bool`	`False`
`use_table_orientation_classify`	Meaning:Whether to use the table orientation classification mode during inference. Description: If enabled, it can correct the direction and correctly complete table recognition when the table in the image has 90/180/270-degree rotation.	`bool`	`True`
`use_ocr_results_with_table_cells`	Meaning:Whether to use the cell-split OCR mode during inference. Description: If enabled, it will split and re-recognize OCR detection results based on the cell prediction results to avoid missing text.	`bool`	`True`

(3) Process the prediction results. The prediction result for each sample is a corresponding Result object, which supports printing, saving as an image, saving as an xlsx file, saving as an HTML file, and saving as a json file:

Method	Description	Parameter	Type	Parameter Description	Default Value
`print()`	Print results to the terminal	`format_json`	`bool`	Whether to format the output content using `JSON` indentation.	`True`
		`indent`	`int`	Specify the indentation level to beautify the output `JSON` data, making it more readable. Effective only when `format_json` is `True`.	4
		`ensure_ascii`	`bool`	Control whether to escape non-`ASCII` characters to `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` keeps the original characters. Effective only when `format_json` is `True`.	`False`
`save_to_json()`	Save results as a json format file	`save_path`	`str`	The path to save the file. When it is a directory, the saved file will be named the same as the input file type.	None
		`indent`	`int`	Specify the indentation level to beautify the output `JSON` data, making it more readable. Effective only when `format_json` is `True`.	4
		`ensure_ascii`	`bool`	Control whether to escape non-`ASCII` characters to `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` keeps the original characters. Effective only when `format_json` is `True`.	`False`
`save_to_img()`	Save results as an image format file	`save_path`	`str`	The path to save the file, supporting directory or file path.	None
`save_to_xlsx()`	Save results as an xlsx format file	`save_path`	`str`	The path to save the file, supporting directory or file path.	None
`save_to_html()`	Save results as an html format file	`save_path`	`str`	The path to save the file, supporting directory or file path.	None

Calling the print() method will print the results to the terminal. The content printed to the terminal is explained as follows:
1. input_path: (str) The input path of the image to be predicted.
2. page_index: (Union[int, None]) If the input is a PDF file, it represents the current page number of the PDF file; otherwise, it is None.
3. model_settings: (Dict[str, bool]) Configuration parameters required for the production line.
  1. use_doc_preprocessor: (bool) Control whether to enable the document preprocessing sub-line.
  2. use_layout_detection: (bool) Control whether to enable the layout detection sub-line.
  3. use_ocr_model: (bool) Control whether to enable the OCR sub-line.
4. layout_det_res: (Dict[str, Union[List[numpy.ndarray], List[float]]]) Output results of the layout detection sub-module. Only exists when use_layout_detection=True
  1. input_path: (Union[str, None]) The image path accepted by the layout detection area module, saved as None when the input is numpy.ndarray.
  2. page_index: (Union[int, None]) If the input is a PDF file, it represents the current page number of the PDF file; otherwise, it is None.
  3. boxes: (List[Dict]) A list of layout seal area detection boxes. Each element in the list contains the following fields
    - cls_id: (int) The class ID of the layout seal area detection box.
    - score: (float) The confidence score of the layout seal area detection box.
    - coordinate: (List[float]) The coordinates of the four vertices of the layout seal area detection box, in the order of x1, y1, x2, y2, representing the top-left x coordinate, top-left y coordinate, bottom-right x coordinate, and bottom-right y coordinate.
5. doc_preprocessor_res: (Dict[str, Union[str, Dict[str, bool], int]]) Output results of the document preprocessing sub-module. Only exists when use_doc_preprocessor=True
  1. input_path: (Union[str, None]) The image path accepted by the document preprocessing area module, saved as None when the input is numpy.ndarray.
  2. page_index: (Union[int, None]) If the input is a PDF file, it represents the current page number of the PDF file; otherwise, it is None.
  3. model_settings: (Dict) Model configuration parameters of the document preprocessing sub-line.
  4. model_settings: (Dict) Model configuration parameters of the document preprocessing sub-line.
    - use_doc_orientation_classify: (bool) Control whether to enable document orientation classification.
    - use_doc_unwarping: (bool) Control whether to enable text image unwarping.
  5. angle: (int) The predicted result of document orientation classification. Enabled values are [0,1,2,3], corresponding to [0°,90°,180°,270°]; disabled value is -1.
6. dt_polys: (List[numpy.ndarray]) List of text detection polygons. Each detection box is represented by a numpy array of shape (4, 2), with data type int16.
7. dt_scores: (List[float]) List of text detection box confidence scores
8. text_det_params: (Dict[str, Dict[str, int, float]]) Configuration parameters for the text detection module.
  1. limit_side_len: (int) Side length limit value during image preprocessing.
  2. limit_type: (str) Type of side length limit processing.
  3. thresh: (float) Confidence threshold for text pixel classification.
  4. box_thresh: (float) Confidence threshold for text detection boxes.
  5. unclip_ratio: (float) Expansion coefficient for text detection boxes.
  6. text_type: (str) Type of text detection, currently fixed as "general".
9. text_rec_score_thresh: (float) Confidence threshold for text recognition results filtering.
10. rec_texts: (List[str]) List of text recognition results, only containing texts with confidence scores higher than text_rec_score_thresh.
11. rec_scores: (List[float]) List of text recognition confidence scores, filtered by text_rec_score_thresh.
12. rec_polys: (List[numpy.ndarray]) List of text detection boxes after confidence filtering, in the same format as dt_polys.
13. rec_boxes: (numpy.ndarray) Array of rectangular bounding boxes for text detection boxes, with shape (n, 4) and dtype int16. Each row represents the coordinates of a rectangular box in the format [x_min, y_min, x_max, y_max], where (x_min, y_min) is the top-left coordinate and (x_max, y_max) is the bottom-right coordinate.
Calling the save_to_json() method will save the above content to the specified save_path. If a directory is specified, the saved path will be save_path/{your_img_basename}_res.json; if a file is specified, it will be saved directly to that file. Since json files do not support saving numpy arrays, the numpy.array types will be converted to list format.
Calling the save_to_img() method will save the visualization results to the specified save_path. If a directory is specified, the saved path will be save_path/{your_img_basename}_ocr_res_img.{your_img_extension}; if a file is specified, it will be saved directly to that file. (The pipeline usually contains many result images, so it is not recommended to specify a specific file path directly, as multiple images will be overwritten and only the last image will be retained.)
Calling the save_to_html() method will save the above content to the specified save_path. If a directory is specified, the saved path will be save_path/{your_img_basename}_table_1.html; if a file is specified, it will be saved directly to that file. In the general table recognition V2 pipeline, the HTML format of the table in the image will be written to the specified HTML file.
Calling the save_to_xlsx() method will save the above content to the specified save_path. If a directory is specified, the saved path will be save_path/{your_img_basename}_res.xlsx; if a file is specified, it will be saved directly to that file. In the general table recognition V2 pipeline, the Excel format of the table in the image will be written to the specified xlsx file.

Attribute	Description
`json`	Get the prediction results in `json` format
`img`	Get visualization images in `dict` format

The prediction result obtained by the json attribute is of dict type, and the relevant content is consistent with the content saved by calling the save_to_json() method.
The prediction result returned by the img attribute is a dictionary type data. The keys are table_res_img、ocr_res_img 、layout_res_img, and preprocessed_img, and the corresponding values are four Image.Image objects, in the order of: visualization image of the table recognition result, visualization image of the OCR result, visualization image of the layout area detection result, and visualization image of the image preprocessing. If a certain sub-module is not used, the corresponding result image will not be included in the dictionary.

API Reference

The main operations provided by the service are as follows:

HTTP request method is POST.
Both request and response bodies are JSON data (JSON objects).
When the request is processed successfully, the response status code is 200, and the properties of the response body are as follows:

Name	Type	Meaning
`logId`	`string`	Request UUID.
`errorCode`	`integer`	Error code. Fixed to `0`.
`errorMsg`	`string`	Error description. Fixed to `"Success"`.
`result`	`object`	Operation result.

When the request processing is unsuccessful, the properties of the response body are as follows:

Name	Type	Meaning
`logId`	`string`	Request UUID.
`errorCode`	`integer`	Error code. Same as the response status code.
`errorMsg`	`string`	Error description.

The main operation provided by the service is as follows:

infer

Locate and identify tables in the image.

POST /table-recognition

The properties of the request body are as follows:

Name	Type	Meaning	Required
`file`	`string`	URL of an accessible image file or PDF file on the server, or the Base64 encoded result of the content of the above types of files. By default, for PDF files with more than 10 pages, only the first 10 pages will be processed. To lift the page limit, please add the following configuration in the model configuration file: `Serving: extra: max_num_input_imgs: null`	Yes
`fileType`	`integer` \| `null`	File type. `0` represents a PDF file, `1` represents an image file. If this property is not present in the request body, the file type will be inferred from the URL.	No
`useDocOrientationClassify`	`boolean` \| `null`	Please refer to the `use_doc_orientation_classify` parameter description in the `predict` method of the model object.	No
`useDocUnwarping`	`boolean` \| `null`	Please refer to the `use_doc_unwarping` parameter description in the `predict` method of the model object.	No
`useLayoutDetection`	`boolean` \| `null`	Please refer to the `use_layout_detection` parameter description in the `predict` method of the model object.	No
`useOcrModel`	`boolean` \| `null`	Please refer to the `use_ocr_model` parameter description in the `predict` method of the model object.	No
`textDetLimitSideLen`	`integer` \| `null`	Please refer to the `text_det_limit_side_len` parameter description in the `predict` method of the model object.	No
`textDetLimitType`	`string` \| `null`	Please refer to the `text_det_limit_type` parameter description in the `predict` method of the model object.	No
`textDetThresh`	`number` \| `null`	Please refer to the `text_det_thresh` parameter description in the `predict` method of the model object.	No
`textDetBoxThresh`	`number` \| `null`	Please refer to the `text_det_box_thresh` parameter description in the `predict` method of the model object.	No
`textDetUnclipRatio`	`number` \| `null`	Please refer to the `text_det_unclip_ratio` parameter description in the `predict` method of the model object.	No
`textRecScoreThresh`	`number` \| `null`	Please refer to the `text_rec_score_thresh` parameter description in the `predict` method of the model object.	No
`useTableCellsOcrResults`	`boolean`	Please refer to the `use_table_cells_ocr_results` parameter description in the `predict` method of the model object.	No
`useE2eWiredTableRecModel`	`boolean`	Please refer to the `use_e2e_wired_table_rec_model` parameter description in the `predict` method of the model object.	No
`useE2eWirelessTableRecModel`	`boolean`	Please refer to the `use_e2e_wireless_table_rec_model` parameter description in the `predict` method of the model object.	No
`visualize`	`boolean` \| `null`	Whether to return the final visualization image and intermediate images during the processing. If `true` is provided: return images. If `false` is provided: do not return any images. If this parameter is omitted from the request body, or if `null` is explicitly passed, the behavior will follow the value of `Serving.visualize` in the pipeline configuration. For example, adding the following setting to the pipeline config file: `Serving: visualize: False` will disable image return by default. This behavior can be overridden by explicitly setting the `visualize` parameter in the request. If neither the request body nor the configuration file is set (If `visualize` is set to `null` in the request and not defined in the configuration file), the image is returned by default.	No

When the request is processed successfully, the result in the response body has the following properties:

Name	Type	Meaning
`tableRecResults`	`object`	Table recognition results. The length of the array is 1 (for image input) or the actual number of processed document pages (for PDF input). For PDF input, each element in the array represents the result of each processed page in the PDF file.
`dataInfo`	`object`	Input data information.

Each element in tableRecResults is an object with the following properties:

Name	Type	Meaning
`prunedResult`	`object`	A simplified version of the JSON representation of the result generated by the `predict` method of the model object, where the `input_path` and `page_index` fields are removed.
`outputImages`	`object` \| `null`	Refer to the `img` property description of the model prediction results. The images are in JPEG format and encoded in Base64.
`inputImage`	`string` \| `null`	Input image. The image is in JPEG format and encoded in Base64.

Multilingual Service Call Examples

Python

import base64
import requests

API_URL = "http://localhost:8080/table-recognition"
file_path = "./demo.jpg"

with open(file_path, "rb") as file:
    file_bytes = file.read()
    file_data = base64.b64encode(file_bytes).decode("ascii")

payload = {"file": file_data, "fileType": 1}

response = requests.post(API_URL, json=payload)

assert response.status_code == 200
result = response.json()["result"]
for i, res in enumerate(result["tableRecResults"]):
    print(res["prunedResult"])
    for img_name, img in res["outputImages"].items():
        img_path = f"{img_name}_{i}.jpg"
        with open(img_path, "wb") as f:
            f.write(base64.b64decode(img))
        print(f"Output image saved at {img_path}")

C++

#include <iostream>
#include <fstream>
#include <vector>
#include <string>
#include "cpp-httplib/httplib.h" // https://github.com/Huiyicc/cpp-httplib
#include "nlohmann/json.hpp" // https://github.com/nlohmann/json
#include "base64.hpp" // https://github.com/tobiaslocker/base64

int main() {
    httplib::Client client("localhost", 8080);
    const std::string filePath = "./demo.jpg";
    std::ifstream file(filePath, std::ios::binary | std::ios::ate);
    if (!file) {
        std::cerr << "Error opening file." << std::endl;
        return 1;
    }

    std::streamsize size = file.tellg();
    file.seekg(0, std::ios::beg);
    std::vector buffer(size);

    if (!file.read(buffer.data(), size)) {
        std::cerr << "Error reading file." << std::endl;
        return 1;
    }

    std::string bufferStr(buffer.data(), static_cast(size));
    std::string encodedFile = base64::to_base64(bufferStr);

    nlohmann::json jsonObj;
    jsonObj["file"] = encodedFile;
    jsonObj["fileType"] = 1;

    auto response = client.Post("/table-recognition", jsonObj.dump(), "application/json");

    if (response && response->status == 200) {
        nlohmann::json jsonResponse = nlohmann::json::parse(response->body);
        auto result = jsonResponse["result"];

        if (!result.is_object() || !result["tableRecResults"].is_array()) {
            std::cerr << "Unexpected response structure." << std::endl;
            return 1;
        }

        for (size_t i = 0; i < result["tableRecResults"].size(); ++i) {
            auto tableRecResult = result["tableRecResults"][i];
            std::cout << tableRecResult["prunedResult"] << std::endl;

            if (tableRecResult["outputImages"].is_object()) {
                for (auto& img : tableRecResult["outputImages"].items()) {
                    std::string imgName = img.key();
                    std::string encodedImage = img.value();
                    std::string decodedImage = base64::from_base64(encodedImage);

                    std::string imgPath = imgName + "_" + std::to_string(i) + ".jpg";
                    std::ofstream outputImage(imgPath, std::ios::binary);
                    if (outputImage.is_open()) {
                        outputImage.write(decodedImage.c_str(), static_cast(decodedImage.size()));
                        outputImage.close();
                        std::cout << "Output image saved at " << imgPath << std::endl;
                    } else {
                        std::cerr << "Unable to open file for writing: " << imgPath << std::endl;
                    }
                }
            }
        }
    } else {
        std::cerr << "Failed to send HTTP request." << std::endl;
        if (response) {
            std::cerr << "HTTP status code: " << response->status << std::endl;
            std::cerr << "Response body: " << response->body << std::endl;
        }
        return 1;
    }

    return 0;
}

Java

import okhttp3.*;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.node.ObjectNode;

import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Base64;

public class Main {
    public static void main(String[] args) throws IOException {
        String API_URL = "http://localhost:8080/table-recognition";
        String imagePath = "./demo.jpg";

        File file = new File(imagePath);
        byte[] fileContent = java.nio.file.Files.readAllBytes(file.toPath());
        String base64Image = Base64.getEncoder().encodeToString(fileContent);

        ObjectMapper objectMapper = new ObjectMapper();
        ObjectNode payload = objectMapper.createObjectNode();
        payload.put("file", base64Image);
        payload.put("fileType", 1);

        OkHttpClient client = new OkHttpClient();
        MediaType JSON = MediaType.get("application/json; charset=utf-8");
        RequestBody body = RequestBody.create(JSON, payload.toString());

        Request request = new Request.Builder()
                .url(API_URL)
                .post(body)
                .build();

        try (Response response = client.newCall(request).execute()) {
            if (response.isSuccessful()) {
                String responseBody = response.body().string();
                JsonNode root = objectMapper.readTree(responseBody);
                JsonNode result = root.get("result");

                JsonNode tableRecResults = result.get("tableRecResults");
                for (int i = 0; i < tableRecResults.size(); i++) {
                    JsonNode item = tableRecResults.get(i);

                    JsonNode prunedResult = item.get("prunedResult");
                    System.out.println("Pruned Result [" + i + "]: " + prunedResult.toString());

                    JsonNode outputImages = item.get("outputImages");

                    outputImages.fieldNames().forEachRemaining(imgName -> {
                        String imgBase64 = outputImages.get(imgName).asText();
                        byte[] imgBytes = Base64.getDecoder().decode(imgBase64);
                        String imgPath = "output_" + imgName +  ".jpg";
                        try (FileOutputStream fos = new FileOutputStream(imgPath)) {
                            fos.write(imgBytes);
                            System.out.println("Saved image to: " + imgPath);
                        } catch (IOException e) {
                            e.printStackTrace();
                        }
                    });
                }
            } else {
                System.err.println("Request failed with HTTP code: " + response.code());
            }
        }
    }
}

package main

import (
    "bytes"
    "encoding/base64"
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
)

func main() {
    API_URL := "http://localhost:8080/table-recognition"
    filePath := "./demo.jpg"

    fileBytes, err := ioutil.ReadFile(filePath)
    if err != nil {
        fmt.Printf("Error reading file: %v\n", err)
        return
    }
    fileData := base64.StdEncoding.EncodeToString(fileBytes)

    payload := map[string]interface{}{
        "file":     fileData,
        "fileType": 1,
    }
    payloadBytes, err := json.Marshal(payload)
    if err != nil {
        fmt.Printf("Error marshaling payload: %v\n", err)
        return
    }

    client := &http.Client{}
    req, err := http.NewRequest("POST", API_URL, bytes.NewBuffer(payloadBytes))
    if err != nil {
        fmt.Printf("Error creating request: %v\n", err)
        return
    }
    req.Header.Set("Content-Type", "application/json")

    res, err := client.Do(req)
    if err != nil {
        fmt.Printf("Error sending request: %v\n", err)
        return
    }
    defer res.Body.Close()

    if res.StatusCode != http.StatusOK {
        fmt.Printf("Unexpected status code: %d\n", res.StatusCode)
        return
    }

    body, err := ioutil.ReadAll(res.Body)
    if err != nil {
        fmt.Printf("Error reading response body: %v\n", err)
        return
    }

    type TableRecResult struct {
        PrunedResult  map[string]interface{} `json:"prunedResult"`
        OutputImages  map[string]string      `json:"outputImages"`
        InputImage    *string                `json:"inputImage"`
    }

    type Response struct {
        Result struct {
            TableRecResults []TableRecResult `json:"tableRecResults"`
            DataInfo        interface{}      `json:"dataInfo"`
        } `json:"result"`
    }

    var respData Response
    if err := json.Unmarshal(body, &respData); err != nil {
        fmt.Printf("Error unmarshaling response: %v\n", err)
        return
    }

    for i, res := range respData.Result.TableRecResults {
        fmt.Printf("Result %d - prunedResult: %+v\n", i, res.PrunedResult)

        for imgName, imgData := range res.OutputImages {
            imgBytes, err := base64.StdEncoding.DecodeString(imgData)
            if err != nil {
                fmt.Printf("Error decoding image %s_%d: %v\n", imgName, i, err)
                continue
            }

            filename := fmt.Sprintf("%s_%d.jpg", imgName, i)
            if err := ioutil.WriteFile(filename, imgBytes, 0644); err != nil {
                fmt.Printf("Error saving image %s: %v\n", filename, err)
                continue
            }
            fmt.Printf("Saved image to %s\n", filename)
        }
    }
}

using System;
using System.IO;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;
using Newtonsoft.Json.Linq;

class Program
{
    static readonly string API_URL = "http://localhost:8080/table-recognition";
    static readonly string inputFilePath = "./demo.jpg";

    static async Task Main(string[] args)
    {
        var httpClient = new HttpClient();

        byte[] fileBytes = File.ReadAllBytes(inputFilePath);
        string fileData = Convert.ToBase64String(fileBytes);

        var payload = new JObject
        {
            { "file", fileData },
            { "fileType", 1 }
        };
        var content = new StringContent(payload.ToString(), Encoding.UTF8, "application/json");

        HttpResponseMessage response = await httpClient.PostAsync(API_URL, content);
        response.EnsureSuccessStatusCode();

        string responseBody = await response.Content.ReadAsStringAsync();
        JObject jsonResponse = JObject.Parse(responseBody);

        JArray tableRecResults = (JArray)jsonResponse["result"]["tableRecResults"];
        for (int i = 0; i < tableRecResults.Count; i++)
        {
            var res = tableRecResults[i];
            Console.WriteLine($"[{i}] prunedResult:\n{res["prunedResult"]}");

            JObject outputImages = res["outputImages"] as JObject;
            if (outputImages != null)
            {
                foreach (var img in outputImages)
                {
                    string imgName = img.Key;
                    string base64Img = img.Value?.ToString();
                    if (!string.IsNullOrEmpty(base64Img))
                    {
                        string imgPath = $"{imgName}_{i}.jpg";
                        byte[] imageBytes = Convert.FromBase64String(base64Img);
                        File.WriteAllBytes(imgPath, imageBytes);
                        Console.WriteLine($"Output image saved at {imgPath}");
                    }
                }
            }
        }
    }
}

Node.js

const axios = require('axios');
const fs = require('fs');
const path = require('path');

const API_URL = 'http://localhost:8080/table-recognition';
const inputImagePath = './demo.jpg';

function encodeImageToBase64(filePath) {
  const bitmap = fs.readFileSync(filePath);
  return Buffer.from(bitmap).toString('base64');
}

async function callTableRecognitionAPI() {
  const payload = {
    file: encodeImageToBase64(inputImagePath),
    fileType: 1
  };

  try {
    const response = await axios.post(API_URL, payload, {
      headers: {
        'Content-Type': 'application/json'
      },
      maxBodyLength: Infinity
    });

    const results = response.data.result.tableRecResults;

    results.forEach((res, index) => {
      console.log(`Result [${index}] prunedResult:\n`, res.prunedResult);

      const outputImages = res.outputImages || {};
      Object.entries(outputImages).forEach(([imgName, base64Img]) => {
        const outputPath = `${imgName}_${index}.jpg`;
        fs.writeFileSync(outputPath, Buffer.from(base64Img, 'base64'));
        console.log(`Saved image: ${outputPath}`);
      });
    });

  } catch (error) {
    console.error('API request failed:', error.message);
  }
}

callTableRecognitionAPI();

PHP

<?php

$API_URL = "http://localhost:8080/table-recognition";
$image_path = "./demo.jpg";

$image_data = base64_encode(file_get_contents($image_path));
$payload = array(
    "file" => $image_data,
    "fileType" => 1
);

$ch = curl_init($API_URL);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_HTTPHEADER, array('Content-Type: application/json'));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);

$result_array = json_decode($response, true);
$results = $result_array["result"]["tableRecResults"];

foreach ($results as $i => $item) {
    echo "[$i] prunedResult:\n";
    print_r($item["prunedResult"]);

    if (!empty($item["outputImages"])) {
        foreach ($item["outputImages"] as $img_name => $base64_img) {
            $img_path = $img_name . "_" . $i . ".jpg";
            file_put_contents($img_path, base64_decode($base64_img));
            echo "Output image saved at $img_path\n";
        }
    } else {
        echo "No outputImages found for item $i\n";
    }
}
?>

fine-tuning

your own domain-specific or application scenario data

Situation	Fine-Tuning Module	Fine-Tuning Reference Link
Table classification error	Table Classification Module	Link
Table cell location error	Table Cell Detection Module	Link
Table structure recognition error	Table Structure Recognition Module	Link
Failed to detect the area where the table is located	Layout Area Detection Module	Link
Text detection missed	Text Detection Module	Link
Incorrect text content	Text Recognition Module	Link
Overall image rotation/table rotation correction is inaccurate	Document Image Orientation Classification Module	Link
Image distortion correction is inaccurate	Text Image Correction Module	Fine-tuning not supported