RARE¶

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 Cloneto 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:

```bash linenums="1" 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:

```bash linenums="1" 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:

text linenums="1" 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¶

Citation¶

bibtex @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}, }