DocumentAIKit/doc/doc_en/algorithm_rec_rosetta_en.md

Rosetta

• 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:

Rosetta: Large Scale System for Text Detection and Recognition in Images Borisyuk F , Gordo A , V Sivakumar KDD, 2018

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
Rosetta	Resnet34_vd	configs/rec/rec_r34_vd_none_none_ctc.yml	79.11%	training model
Rosetta	MobileNetV3	configs/rec/rec_mv3_none_none_ctc.yml	75.80%	training 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_none_none_ctc.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_none_none_ctc.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_none_none_ctc.yml -o Global.pretrained_model={path/to/weights}/best_accuracy

3.3 Prediction

python3 tools/infer_rec.py -c configs/rec/rec_r34_vd_none_none_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, convert the model saved during the Rosetta 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_none_none_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_none_ctc_v2.0_train/best_accuracy Global.save_inference_dir=./inference/rec_rosetta

Rosetta 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_rosetta/" --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', 0.9999982714653015)

4.2 C++ Inference

Not currently supported

4.3 Serving

Not currently supported

4.4 More

The Rosetta model also supports the following inference deployment methods:

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

5. FAQ

Quote

@inproceedings{2018Rosetta,
  title={Rosetta: Large Scale System for Text Detection and Recognition in Images},
  author={ Borisyuk, Fedor and Gordo, Albert and Sivakumar, Viswanath },
  booktitle={the 24th ACM SIGKDD International Conference},
  year={2018},
}