DocumentAIKit/doc/doc_en/algorithm_rec_sar_en.md

SAR

• 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:

Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition Hui Li, Peng Wang, Chunhua Shen, Guyu Zhang AAAI, 2019

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
SAR	ResNet31	rec_r31_sar.yml	87.20%	train model

Note:In addition to using the two text recognition datasets MJSynth and SynthText, SynthAdd data (extraction code: 627x), and some real data are used in training, the specific data details can refer to the paper.

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_r31_sar.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_r31_sar.yml

Evaluation:

# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r31_sar.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_r31_sar.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 SAR 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_r31_sar.yml -o Global.pretrained_model=./rec_r31_sar_train/best_accuracy  Global.save_inference_dir=./inference/rec_sar

For SAR 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_sar/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SAR" --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

Citation

@article{Li2019ShowAA,
  title={Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition},
  author={Hui Li and Peng Wang and Chunhua Shen and Guyu Zhang},
  journal={ArXiv},
  year={2019},
  volume={abs/1811.00751}
}