Table Recognition

This article provides a full-process guide for the PaddleOCR table recognition model, including data preparation, model training, tuning, evaluation, prediction, and detailed descriptions of each stage:

1. Data Preparation
2. Training
3. Evaluation and Test
- 3.1. Evaluation
- 3.2. Test table structure recognition effect
4. Model export and prediction
- 4.1 Model export
- 4.2 Prediction
5. FAQ

1. Data Preparation

1.1. DataSet Format

The format of the PaddleOCR table recognition model dataset is as follows:

img_label # Each image is marked with a string after json.dumps()
...
img_label

The json format of each line is:

{
   'filename': PMC5755158_010_01.png,# image name
   'split': ’train‘, # whether the image belongs to the training set or the validation set
   'imgid': 0,# index of image
   'html': {
     'structure': {'tokens': ['<thead>', '<tr>', '<td>', ...]}, # HTML string of the table
     'cells': [
       {
         'tokens': ['P', 'a', 'd', 'd', 'l', 'e', 'P', 'a', 'd', 'd', 'l', 'e'], # text in cell
         'bbox': [x0, y0, x1, y1] # bbox of cell
       }
     ]
   }
}

The default storage path for training data is PaddleOCR/train_data, if you already have a dataset on disk, just create a soft link to the dataset directory:

# linux and mac os
ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
# windows
mklink /d <path/to/paddle_ocr>/train_data/dataset <path/to/dataset>

1.2. Data Download

Download the public dataset reference table_datasets。

1.3. Dataset Generation

Use TableGeneration to generate scanned table images.

TableGeneration is an open source table dataset generation tool, which renders html strings through browser rendering to obtain table images.

Some samples are as follows:

Type	Sample
Simple Table
Simple Color Table

2. Training

PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the SLANet model will be used as an example:

2.1. Start Training

If you are installing the cpu version, please modify the use_gpu field in the configuration file to false

# GPU training Support single card and multi-card training
# The training log will be automatically saved as train.log under "{save_model_dir}"

# specify the single card training(Long training time, not recommended)
python3 tools/train.py -c configs/table/SLANet.yml

# specify the card number through --gpus
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/table/SLANet.yml

After starting training normally, you will see the following log output:

[2022/08/16 03:07:33] ppocr INFO: epoch: [1/400], global_step: 20, lr: 0.000100, acc: 0.000000, loss: 3.915012, structure_loss: 3.229450, loc_loss: 0.670590, avg_reader_cost: 2.63382 s, avg_batch_cost: 6.32390 s, avg_samples: 48.0, ips: 7.59025 samples/s, eta: 9 days, 2:29:27
[2022/08/16 03:08:41] ppocr INFO: epoch: [1/400], global_step: 40, lr: 0.000100, acc: 0.000000, loss: 1.750859, structure_loss: 1.082116, loc_loss: 0.652822, avg_reader_cost: 0.02533 s, avg_batch_cost: 3.37251 s, avg_samples: 48.0, ips: 14.23271 samples/s, eta: 6 days, 23:28:43
[2022/08/16 03:09:46] ppocr INFO: epoch: [1/400], global_step: 60, lr: 0.000100, acc: 0.000000, loss: 1.395154, structure_loss: 0.776803, loc_loss: 0.625030, avg_reader_cost: 0.02550 s, avg_batch_cost: 3.26261 s, avg_samples: 48.0, ips: 14.71214 samples/s, eta: 6 days, 5:11:48

The following information is automatically printed in the log:

PaddleOCR supports alternating training and evaluation. You can modify eval_batch_step in configs/table/SLANet.yml to set the evaluation frequency. By default, it is evaluated once every 1000 iters. During the evaluation process, the best acc model is saved as output/SLANet/best_accuracy by default.

If the validation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or perform evaluation after training.

Tips: You can use the -c parameter to select various model configurations under the configs/table/ path for training. For the table recognition algorithms supported by PaddleOCR, please refer to Table Algorithms List:

**Note that the configuration file for prediction/evaluation must be the same as training. **

2.2. Resume Training

If the training program is interrupted, if you want to load the interrupted model to resume training, you can specify the path of the model to be loaded by specifying Global.checkpoints:

python3 tools/train.py -c configs/table/SLANet.yml -o Global.checkpoints=./your/trained/model

Note: The priority of Global.checkpoints is higher than that of Global.pretrained_model, that is, when two parameters are specified at the same time, the model specified by Global.checkpoints will be loaded first. If Global.checkpoints The specified model path is incorrect, and the model specified by Global.pretrained_model will be loaded.

2.3. Training with New Backbone

The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under ppocr/modeling. The data entering the network will pass through these four parts in sequence(transforms->backbones-> necks->heads).

├── architectures # Code for building network
├── transforms    # Image Transformation Module
├── backbones     # Feature extraction module
├── necks         # Feature enhancement module
└── heads         # Output module

If the Backbone to be replaced has a corresponding implementation in PaddleOCR, you can directly modify the parameters in the Backbone part of the configuration yml file.

However, if you want to use a new Backbone, an example of replacing the backbones is as follows:

Create a new file under the ppocr/modeling/backbones folder, such as my_backbone.py.

Add code in the my_backbone.py file, the sample code is as follows:

import paddle
import paddle.nn as nn
import paddle.nn.functional as F


class MyBackbone(nn.Layer):
def __init__(self, *args, **kwargs):
    super(MyBackbone, self).__init__()
    # your init code
    self.conv = nn.xxxx

def forward(self, inputs):
    # your network forward
    y = self.conv(inputs)
    return y

Import the added module in the ppocr/modeling/backbones/_init_.py file.

After adding the four-part modules of the network, you only need to configure them in the configuration file to use, such as:

  Backbone:
    name: MyBackbone
    args1: args1

NOTE: More details about replace Backbone and other mudule can be found in doc.

2.4. Mixed Precision Training

If you want to speed up your training further, you can use Auto Mixed Precision Training, taking a single machine and a single gpu as an example, the commands are as follows:

python3 tools/train.py -c configs/table/SLANet.yml \
     -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy \
     Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True

2.5. Distributed Training

During multi-machine multi-gpu training, use the --ips parameter to set the used machine IP address, and the --gpus parameter to set the used GPU ID:

python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/table/SLANet.yml \
     -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy

Note: (1) When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other. (2) Training needs to be launched separately on multiple machines. The command to view the ip address of the machine is ifconfig. (3) For more details about the distributed training speedup ratio, please refer to Distributed Training Tutorial.

2.6. Training on other platform(Windows/macOS/Linux DCU)

Windows GPU/CPU The Windows platform is slightly different from the Linux platform: Windows platform only supports single gpu training and inference, specify GPU for training set CUDA_VISIBLE_DEVICES=0 On the Windows platform, DataLoader only supports single-process mode, so you need to set num_workers to 0;
macOS GPU mode is not supported, you need to set use_gpu to False in the configuration file, and the rest of the training evaluation prediction commands are exactly the same as Linux GPU.
Linux DCU Running on a DCU device requires setting the environment variable export HIP_VISIBLE_DEVICES=0,1,2,3, and the rest of the training and evaluation prediction commands are exactly the same as the Linux GPU.

2.7. Fine-tuning

2.7.1 Dataset

Data number: It is recommended to prepare at least 2000 table recognition datasets for model fine-tuning.

2.7.2 model selection

It is recommended to choose the SLANet model (configuration file: SLANet_ch.yml, pre-training model: ch_ppstructure_mobile_v2.0_SLANet_train.tar) for fine-tuning, its accuracy and generalization performance is the best Chinese table pre-training model currently available.

For more table recognition models, please refer to PP-Structure Series Model Library.

2.7.3 Training hyperparameter selection

When fine-tuning the model, the most important hyperparameters are the pretrained model path pretrained_model, the learning rate learning_rate, and some configuration files are shown below.

Global:
  pretrained_model: ./ch_ppstructure_mobile_v2.0_SLANet_train/best_accuracy.pdparams # Pre-trained model path
Optimizer:
  lr:
    name: Cosine
    learning_rate: 0.001 #
    warmup_epoch: 0
  regularizer:
    name: 'L2'
    factor: 0

In the above configuration file, you first need to specify the pretrained_model field as the best_accuracy.pdparams file path.

The configuration file provided by PaddleOCR is for 4-card training (equivalent to a total batch size of 4*48=192) and no pre-trained model is loaded. Therefore, in your scenario, the learning rate is the same as the total The batch size needs to be adjusted linearly, for example

If your scenario is single card training, single card batch_size=48, then the total batch_size=48, it is recommended to adjust the learning rate to about 0.00025.
If your scenario is for single-card training, due to memory limitations, you can only set batch_size=32 for a single card, then the total batch_size=32, it is recommended to adjust the learning rate to about 0.00017.

3. Evaluation and Test

3.1. Evaluation

The model parameters during training are saved in the Global.save_model_dir directory by default. When evaluating metrics, you need to set Global.checkpoints to point to the saved parameter file. Evaluation datasets can be modified via the label_file_list setting in Eval via configs/table/SLANet.yml.

# GPU evaluation, Global.checkpoints is the weight to be tested
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/table/SLANet.yml -o Global.checkpoints={path/to/weights}/best_accuracy

After the operation is completed, the acc indicator of the model will be output. If you evaluate the English table recognition model, you will see the following output.

[2022/08/16 07:59:55] ppocr INFO: acc:0.7622245132160782
[2022/08/16 07:59:55] ppocr INFO: fps:30.991640622573044

3.2. Test table structure recognition effect

Using the model trained by PaddleOCR, you can quickly get prediction through the following script.

The default prediction picture is stored in infer_img, and the trained weight is specified via -o Global.checkpoints:

According to the save_model_dir and save_epoch_step fields set in the configuration file, the following parameters will be saved:

output/SLANet/
├── best_accuracy.pdopt  
├── best_accuracy.pdparams  
├── best_accuracy.states  
├── config.yml  
├── latest.pdopt  
├── latest.pdparams  
├── latest.states  
└── train.log

Among them, best_accuracy.* is the best model on the evaluation set; latest.* is the model of the last epoch.

# Predict table image
python3 tools/infer_table.py -c configs/table/SLANet.yml -o Global.pretrained_model={path/to/weights}/best_accuracy  Global.infer_img=ppstructure/docs/table/table.jpg

Input image:

Get the prediction result of the input image:

['<html>', '<body>', '<table>', '<thead>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</thead>', '<tbody>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '<tr>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '<td></td>', '</tr>', '</tbody>', '</table>', '</body>', '</html>'],[[320.0562438964844, 197.83375549316406, 350.0928955078125, 214.4309539794922], ... , [318.959228515625, 271.0166931152344, 353.7394104003906, 286.4538269042969]]

The cell coordinates are visualized as

4. Model export and prediction

4.1 Model export

inference model (model saved by paddle.jit.save) Generally, it is model training, a solidified model that saves the model structure and model parameters in a file, and is mostly used to predict deployment scenarios. The model saved during the training process is the checkpoints model, and only the parameters of the model are saved, which are mostly used to resume training. Compared with the checkpoints model, the inference model will additionally save the structural information of the model. It has superior performance in predicting deployment and accelerating reasoning, and is flexible and convenient, and is suitable for actual system integration.

The way to convert the form recognition model to the inference model is the same as the text detection and recognition, as follows:

# -c Set the training algorithm yml configuration file
# -o Set optional parameters
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# Global.save_inference_dir Set the address where the converted model will be saved.

python3 tools/export_model.py -c configs/table/SLANet.yml -o Global.pretrained_model=./pretrain_models/SLANet/best_accuracy  Global.save_inference_dir=./inference/SLANet/

After the conversion is successful, there are three files in the model save directory:

inference/SLANet/
    ├── inference.pdiparams         # The parameter file of inference model
    ├── inference.pdiparams.info    # The parameter information of inference model, which can be ignored
    └── inference.pdmodel           # The program file of model

4.2 Prediction

After the model is exported, use the following command to complete the prediction of the inference model

python3.7 table/predict_structure.py \
    --table_model_dir={path/to/inference model} \
    --table_char_dict_path=../ppocr/utils/dict/table_structure_dict_ch.txt \
    --image_dir=docs/table/table.jpg \
    --output=../output/table