Table Classification Module Usage Tutorial¶

1. Overview¶

The Table Classification Module is a key component in computer vision systems, responsible for classifying input table images. The performance of this module directly affects the accuracy and efficiency of the entire table recognition process. The Table Classification Module typically receives table images as input and, using deep learning algorithms, classifies them into predefined categories based on the characteristics and content of the images, such as wired and wireless tables. The classification results from the Table Classification Module serve as output for use in table recognition pipelines.

2. Supported Model List¶

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 (M)
PP-LCNet_x1_0_table_cls	Inference Model/Training Model	94.2	2.35 / 0.47	4.03 / 1.35	6.6M

Test Environment Description:

Performance Test Environment
- Test Dataset: Internal evaluation dataset built by PaddleX.
- Hardware Configuration:
  - GPU: NVIDIA Tesla T4
  - CPU: Intel Xeon Gold 6271C @ 2.60GHz
  - Other Environment: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2
Inference Mode Explanation

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	Choose the optimal prior backend (Paddle/OpenVINO/TRT, etc.)

3. Quick Start¶

❗ Before starting quickly, please first install the PaddleOCR wheel package. For details, please refer to the installation tutorial.

You can quickly experience it with one command:

paddleocr table_classification -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg

You can also integrate model inference from the table classification module into your project. Before running the following code, please download the sample image locally.

from paddleocr import TableClassification
model = TableClassification(model_name="PP-LCNet_x1_0_table_cls")
output = model.predict("table_recognition.jpg", batch_size=1)
for res in output:
    res.print(json_format=False)
    res.save_to_json("./output/res.json")

After running, the result obtained is:

{'res': {'input_path': 'table_recognition.jpg', 'page_index': None, 'class_ids': array([0, 1], dtype=int32), 'scores': array([0.84421, 0.15579], dtype=float32), 'label_names': ['wired_table', 'wireless_table']}}

The parameter meanings are as follows: - input_path: Path of the input image - page_index: If the input is a PDF file, it indicates which page of the PDF it is; otherwise, it is None - class_ids: Class IDs of the prediction results - scores: Confidence scores of the prediction results - label_names: Class names of the prediction results

The visualized image is as follows:

The relevant methods, parameters, etc., are described as follows:

TableClassification instantiates the table classification model (taking PP-LCNet_x1_0_table_cls as an example here), with specific explanations as follows:

Parameter	Description	Type	Default
`model_name`	Name of the model	`str`	`PP-LCNet_x1_0_doc_ori`
`model_dir`	Model storage path	`str`	`None`
`device`	Device(s) to use for inference. Examples: `cpu`, `gpu`, `npu`, `gpu:0`, `gpu:0,1`. If multiple devices are specified, inference will be performed in parallel. Note that parallel inference is not always supported. By default, GPU 0 will be used if available; otherwise, the CPU will be used.	`str`	`None`
`enable_hpi`	Whether to use the high performance inference.	`bool`	`False`
`use_tensorrt`	Whether to use the Paddle Inference TensorRT subgraph engine.	`bool`	`False`
`min_subgraph_size`	Minimum subgraph size for TensorRT when using the Paddle Inference TensorRT subgraph engine.	`int`	`3`
`precision`	Precision for TensorRT when using the Paddle Inference TensorRT subgraph engine. Options: `fp32`, `fp16`, etc.	`str`	`fp32`
`enable_mkldnn`	Whether to use MKL-DNN acceleration for inference.	`bool`	`True`
`cpu_threads`	Number of threads to use for inference on CPUs.	`int`	`10`

Among them, model_name must be specified. After specifying model_name, the default model parameters built into PaddleX are used. When model_dir is specified, the user-defined model is used.
Call the predict() method of the table classification model for inference prediction. This method will return a result list. Additionally, this module also provides a predict_iter() method. Both methods are consistent in terms of parameter acceptance and result return. The difference is that predict_iter() returns a generator, which can process and obtain prediction results step by step, suitable for handling large datasets or scenarios where memory saving is desired. You can choose to use either of these methods according to your actual needs. The predict() method has parameters input and batch_size, with specific explanations as follows:

Parameter	Description	Type	Default
`input`	Input data to be predicted. Required. Supports multiple input types: Python Var: e.g., `numpy.ndarray` representing image data str: - Local image or PDF file path: `/root/data/img.jpg`; - URL of image or PDF file: e.g., example; - Local directory: directory containing images for prediction, e.g., `/root/data/` (Note: directories containing PDF files are not supported; PDFs must be specified by exact file path) List: Elements must be of the above types, e.g., `[numpy.ndarray, numpy.ndarray]`, `["/root/data/img1.jpg", "/root/data/img2.jpg"]`, `["/root/data1", "/root/data2"]`	`Python Var\|str\|list`
`batch_size`	Batch size, positive integer.	`int`	1

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

Method	Description	Parameter	Type	Parameter Description	Default Value
`print()`	Print result to terminal	`format_json`	`bool`	Whether to format the output content using `JSON` indentation	`True`
		`indent`	`int`	Specifies the indentation level to beautify the output `JSON` data, making it more readable, effective only when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Controls whether to escape non-`ASCII` characters into `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` will retain the original characters, effective only when `format_json` is `True`	`False`
`save_to_json()`	Save the result as a json format file	`save_path`	`str`	The path to save the file. When specified as a directory, the saved file is named consistent with the input file type.	None
		`indent`	`int`	Specifies the indentation level to beautify the output `JSON` data, making it more readable, effective only when `format_json` is `True`	4
		`ensure_ascii`	`bool`	Controls whether to escape non-`ASCII` characters into `Unicode`. When set to `True`, all non-`ASCII` characters will be escaped; `False` will retain the original characters, effective only when `format_json` is `True`	`False`

Additionally, the result can be obtained through attributes that provide the visualized images with results and the prediction results, as follows:

Attribute	Description
`json`	Get the prediction result in `json` format
`img`	Get the visualized image

4. Secondary Development¶

Since PaddleOCR does not directly provide training for the table classification module, if you need to train a table classification model, you can refer to the PaddleX Table Classification Module Secondary Development section for training. The trained model can be seamlessly integrated into the PaddleOCR API for inference.

Table Classification Module Usage Tutorial¶

1. Overview¶

2. Supported Model List¶

3. Quick Start¶

4. Secondary Development¶

5. FAQ¶

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