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Table Recognition

1. pipeline

The table recognition mainly contains three models

  1. Single line text detection-DB
  2. Single line text recognition-CRNN
  3. Table structure and cell coordinate prediction-RARE

The table recognition flow chart is as follows

tableocr_pipeline

  1. The coordinates of single-line text is detected by DB model, and then sends it to the recognition model to get the recognition result.
  2. The table structure and cell coordinates is predicted by RARE model.
  3. The recognition result of the cell is combined by the coordinates, recognition result of the single line and the coordinates of the cell.
  4. The cell recognition result and the table structure together construct the html string of the table.

2. Performance

We evaluated the algorithm on the PubTabNet[1] eval dataset, and the performance is as follows:

Method TEDS(Tree-Edit-Distance-based Similarity)
EDD[2] 88.3
Ours 93.32

3. How to use

3.1 quick start

cd PaddleOCR/ppstructure

# download model
mkdir inference && cd inference
# Download the detection model of the ultra-lightweight table English OCR model and unzip it
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar
# Download the recognition model of the ultra-lightweight table English OCR model and unzip it
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar
# Download the ultra-lightweight English table inch model and unzip it
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
# run
python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table

Note: The above model is trained on the PubLayNet dataset and only supports English scanning scenarios. If you need to identify other scenarios, you need to train the model yourself and replace the three fields det_model_dir, rec_model_dir, table_model_dir.

After running, the excel sheet of each picture will be saved in the directory specified by the output field

3.2 Train

In this chapter, we only introduce the training of the table structure model, For model training of text detection and text recognition, please refer to the corresponding documents

data preparation

The training data uses public data set PubTabNet, Can be downloaded from the official website 。The PubTabNet data set contains about 500,000 images, as well as annotations in html format。

Start training

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

# single GPU training
python3 tools/train.py -c configs/table/table_mv3.yml
# multi-GPU training
# Set the GPU ID used by the '--gpus' parameter.
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/table_mv3.yml

In the above instruction, use -c to select the training to use the configs/table/table_mv3.yml configuration file. For a detailed explanation of the configuration file, please refer to config.

load trained model and continue training

If you expect to load trained model and continue the training again, you can specify the parameter Global.checkpoints as the model path to be loaded.

python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./your/trained/model

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

3.3 Eval

The table uses TEDS(Tree-Edit-Distance-based Similarity) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:

{"PMC4289340_004_00.png": [
  ["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>",  "</tbody>", "</table>", "</body>", "</html>"],
  [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
  [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
]}

In gt json, the key is the image name, the value is the corresponding gt, and gt is a list composed of four items, and each item is

  1. HTML string list of table structure
  2. The coordinates of each cell (not including the empty text in the cell)
  3. The text information in each cell (not including the empty text in the cell)

Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.

cd PaddleOCR/ppstructure
python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json

If the PubLatNet eval dataset is used, it will be output

teds: 93.32

3.4 Inference

cd PaddleOCR/ppstructure
python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table

After running, the excel sheet of each picture will be saved in the directory specified by the output field

Reference

  1. https://github.com/ibm-aur-nlp/PubTabNet
  2. https://arxiv.org/pdf/1911.10683