DocumentAIKit/doc/doc_en/algorithm_rec_rare_en.md

RARE

• 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 information:

Robust Scene Text Recognition with Automatic Rectification Baoguang Shi, Xinggang Wang, Pengyuan Lyu, Cong Yao, Xiang Bai∗ CVPR, 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:

Models	Backbone Networks	Configuration Files	Avg Accuracy	Download Links
RARE	Resnet34_vd	configs/rec/rec_r34_vd_tps_bilstm_att.yml	83.60%	training model
RARE	MobileNetV3	configs/rec/rec_mv3_tps_bilstm_att.yml	82.50%	trained model

2. Environment

Please refer to Operating Environment Preparation to configure the PaddleOCR operating environment, and refer to Project Clone to clone the project code.

3. Model Training / Evaluation / Prediction

Please refer to Text Recognition Training Tutorial. PaddleOCR modularizes the code, and training different recognition models only requires changing the configuration file. Take the backbone network based on Resnet34_vd as an example:

3.1 Training

#Single card training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml
#Multi-card training, specify the card number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml

3.2 Evaluation

# GPU evaluation, Global.pretrained_model is the model to be evaluated
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy

3.3 Prediction

python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png

4. Inference

4.1 Python Inference

First, convert the model saved during the RARE text recognition training process into an inference model. Take the model trained on the MJSynth and SynthText text recognition datasets based on the Resnet34_vd backbone network as an example (Model download address ), which can be converted using the following command:

```shell python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_att.yml -o Global.pretrained_model=./rec_r34_vd_tps_bilstm_att_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_rare

RARE text recognition model inference, you can execute the following commands:

```shell
python3 tools/infer/predict_rec.py --image_dir="doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_rare/" --rec_image_shape="3, 32, 100" --rec_char_dict_path= "./ppocr/utils/ic15_dict.txt"

The inference results are as follows:

Predicts of doc/imgs_words/en/word_1.png:('joint ', 0.9999969601631165)

4.2 C++ Inference

Not currently supported

4.3 Serving

Not currently supported

4.4 More

The RARE model also supports the following inference deployment methods:

• Paddle2ONNX Inference: After preparing the inference model, refer to the paddle2onnx tutorial.

5. FAQ

Quote

@inproceedings{2016Robust,
  title={Robust Scene Text Recognition with Automatic Rectification},
  author={ Shi, B. and Wang, X. and Lyu, P. and Cong, Y. and Xiang, B. },
  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
}