PaddleOcr_v4/doc/doc_en/algorithm_rec_satrn_en.md

SATRN

• 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

论文信息：

On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee CVPR, 2020 Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:

Model	Backbone	config	Acc	Download link
SATRN	ShallowCNN	88.05%	configs/rec/rec_satrn.yml	训练模型

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_satrn.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_satrn.yml

Evaluation:

# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy

Prediction:

# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png

4. Inference and Deployment

4.1 Python Inference

First, the model saved during the SATRN text recognition training process is converted into an inference model. ( Model download link ), you can use the following command to convert:

python3 tools/export_model.py -c configs/rec/rec_satrn.yml -o Global.pretrained_model=./rec_satrn_train/best_accuracy  Global.save_inference_dir=./inference/rec_satrn

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

python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_satrn/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SATRN" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

引用

@article{lee2019recognizing,
      title={On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention},
      author={Junyeop Lee and Sungrae Park and Jeonghun Baek and Seong Joon Oh and Seonghyeon Kim and Hwalsuk Lee},
      year={2019},
      eprint={1910.04396},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}