- 1. Introduction
- 2. Environment
- 3. Model Training / Evaluation / Prediction
- 4. Inference and Deployment
- 5. FAQ
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 |
Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone" to clone the project code.
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
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"
With the inference model prepared, refer to the cpp infer tutorial for C++ inference.
With the inference model prepared, refer to the pdserving tutorial for service deployment by Paddle Serving.
More deployment schemes supported for STAR-Net:
- Paddle2ONNX: with the inference model prepared, please refer to the paddle2onnx tutorial.
@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}
}