PaddleOCR/doc/doc_en/algorithm_rec_can_en.md

RobustScanner

• 1. Introduction
• 2. Environment
• 3. Model Training / Evaluation / Prediction
  • 3.1 Training
  • 3.2 Evaluation
  • 3.3 Prediction
• 4. Inference and Deployment
  • 4.1 Python Inference
  • 4.2 C++ Inference
  • 4.3 Serving
  • 4.4 More
• 5. FAQ

1. Introduction

Paper:

When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition Bohan Li, Ye Yuan, Dingkang Liang, Xiao Liu, Zhilong Ji, Jinfeng Bai, Wenyu Liu, Xiang Bai ECCV, 2022

Using CROHME handwrittem mathematical expression recognition datasets for training, and evaluating on its test sets, the algorithm reproduction effect is as follows:

Model	Backbone	config	exprate	Download link
CAN	DenseNet	rec_d28_can.yml	51.72	coming soon

2. Environment

Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone" to clone the project code.

3. Model Training / Evaluation / Prediction

Please refer to Text Recognition Tutorial. PaddleOCR modularizes the code, and training different recognition models only requires changing the configuration file.

Training:

Specifically, after the data preparation is completed, the training can be started. The training command is as follows:

#Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_d28_can.yml

#Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_d28_can.yml

Evaluation:

# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_d28_can.yml -o Global.pretrained_model=./rec_d28_can_train/best_accuracy

Prediction:

# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_d28_can.yml -o Architecture.Head.attdecoder.is_train=False Global.infer_img='./doc/imgs_hme/hme_01.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy

4. Inference and Deployment

4.1 Python Inference

First, the model saved during the RobustScanner text recognition training process is converted into an inference model. you can use the following command to convert:

python3 tools/export_model.py -c configs/rec/rec_d28_can.yml -o Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False

For RobustScanner text recognition model inference, the following commands can be executed:

python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_hme/hme_01.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_image_shape="1, 132, 519" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

Citation

@misc{https://doi.org/10.48550/arxiv.2207.11463,
  doi = {10.48550/ARXIV.2207.11463},
  url = {https://arxiv.org/abs/2207.11463},
  author = {Li, Bohan and Yuan, Ye and Liang, Dingkang and Liu, Xiao and Ji, Zhilong and Bai, Jinfeng and Liu, Wenyu and Bai, Xiang},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}