DocumentAIKit/doc/doc_en/algorithm_rec_can_en.md

CAN

• 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%	trained model

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.pdparams

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/crohme_demo/hme_00.jpg' Global.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams

4. Inference and Deployment

4.1 Python Inference

First, the model saved during the CAN handwritten mathematical expression 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.pretrained_model=./rec_d28_can_train/best_accuracy.pdparams Global.save_inference_dir=./inference/rec_d28_can/ Architecture.Head.attdecoder.is_train=False

# The default output max length of the model is 36. If you need to predict a longer sequence, please specify its output sequence as an appropriate value when exporting the model, as: Architecture.Head.max_ text_ length=72

For CAN handwritten mathematical expression recognition model inference, the following commands can be executed:

python3 tools/infer/predict_rec.py --image_dir="./doc/datasets/crohme_demo/hme_00.jpg" --rec_algorithm="CAN" --rec_batch_num=1 --rec_model_dir="./inference/rec_d28_can/" --rec_char_dict_path="./ppocr/utils/dict/latex_symbol_dict.txt"

# If you need to predict on a picture with black characters on a white background, please set: -- rec_ image_ inverse=False

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}
}