PaddleOCR/docs/algorithm/text_recognition/algorithm_rec_aster.en.md

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STAR-Net

1. Introduction

Paper:

STAR-Net: a spatial attention residue network for scene text recognition. Wei Liu, Chaofeng Chen, Kwan-Yee K. Wong, Zhizhong Su and Junyu Han. BMVC, pages 43.1-43.13, 2016

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	ACC	config	Download link
---	---	---	---	---
StarNet	Resnet34_vd	84.44%	configs/rec/rec_r34_vd_tps_bilstm_ctc.yml	训练模型
StarNet	MobileNetV3	81.42%	configs/rec/rec_mv3_tps_bilstm_ctc.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_r34_vd_tps_bilstm_ctc.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 rec_r34_vd_tps_bilstm_ctc.yml

Evaluation

# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.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_r34_vd_tps_bilstm_ctc.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 STAR-Net 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_r34_vd_tps_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_ctc_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/rec_starnet

For STAR-Net text recognition model inference, the following commands can be executed:

python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"

4.2 C++ Inference

With the inference model prepared, refer to the cpp infer tutorial for C++ inference.

4.3 Serving

With the inference model prepared, refer to the pdserving tutorial for service deployment by Paddle Serving.

4.4 More

More deployment schemes supported for STAR-Net:

• Paddle2ONNX: with the inference model prepared, please refer to the paddle2onnx tutorial.

5. FAQ

Citation

@inproceedings{liu2016star,
  title={STAR-Net: a spatial attention residue network for scene text recognition.},
  author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
  booktitle={BMVC},
  volume={2},
  pages={7},
  year={2016}
}