# SVTR - [1. Introduction](#1) - [2. Environment](#2) - [3. Model Training / Evaluation / Prediction](#3) - [3.1 Training](#3-1) - [3.2 Evaluation](#3-2) - [3.3 Prediction](#3-3) - [4. Inference and Deployment](#4) - [4.1 Python Inference](#4-1) - [4.2 C++ Inference](#4-2) - [4.3 Serving](#4-3) - [4.4 More](#4-4) - [5. FAQ](#5) ## 1. Introduction Paper: > [SVTR: Scene Text Recognition with a Single Visual Model](https://arxiv.org/abs/2205.00159) > Yongkun Du and Zhineng Chen and Caiyan Jia Xiaoting Yin and Tianlun Zheng and Chenxia Li and Yuning Du and Yu-Gang Jiang > IJCAI, 2022 The accuracy (%) and model files of SVTR on the public dataset of scene text recognition are as follows: * Chinese dataset from [Chinese Benckmark](https://arxiv.org/abs/2112.15093) , and the Chinese training evaluation strategy of SVTR follows the paper. | Model |IC13
857 | SVT |IIIT5k
3000 |IC15
1811| SVTP |CUTE80 | Avg_6 |IC15
2077 |IC13
1015 |IC03
867|IC03
860|Avg_10 | Chinese
scene_test| Download link | |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|:-----:|:-----:|:---------------------------------------------:|:-----:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | SVTR Tiny | 96.85 | 91.34 | 94.53 | 83.99 | 85.43 | 89.24 | 90.87 | 80.55 | 95.37 | 95.27 | 95.70 | 90.13 | 67.90 | [English](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) / [Chinese](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_ch_train.tar) | | SVTR Small | 95.92 | 93.04 | 95.03 | 84.70 | 87.91 | 92.01 | 91.63 | 82.72 | 94.88 | 96.08 | 96.28 | 91.02 | 69.00 | [English](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_small_none_ctc_en_train.tar) / [Chinese](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_small_none_ctc_ch_train.tar) | | SVTR Base | 97.08 | 91.50 | 96.03 | 85.20 | 89.92 | 91.67 | 92.33 | 83.73 | 95.66 | 95.62 | 95.81 | 91.61 | 71.40 | [English](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_base_none_ctc_en_train.tar) / - | | SVTR Large | 97.20 | 91.65 | 96.30 | 86.58 | 88.37 | 95.14 | 92.82 | 84.54 | 96.35 | 96.54 | 96.74 | 92.24 | 72.10 | [English](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_large_none_ctc_en_train.tar) / [Chinese](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_large_none_ctc_ch_train.tar) | ## 2. Environment Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code. #### Dataset Preparation [English dataset download](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) [Chinese dataset download](https://github.com/fudanvi/benchmarking-chinese-text-recognition#download) ## 3. Model Training / Evaluation / Prediction Please refer to [Text Recognition Tutorial](./recognition.md). 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_svtrnet.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 configs/rec/rec_svtrnet.yml ``` Evaluation: You can download the model files and configuration files provided by `SVTR`: [download link](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar), take `SVTR-T` as an example, using the following command to evaluate: ``` # GPU evaluation python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c ./rec_svtr_tiny_none_ctc_en_train/rec_svtr_tiny_6local_6global_stn_en.yml -o Global.pretrained_model=./rec_svtr_tiny_none_ctc_en_train/best_accuracy ``` Prediction: ``` python3 tools/infer_rec.py -c ./rec_svtr_tiny_none_ctc_en_train/rec_svtr_tiny_6local_6global_stn_en.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_svtr_tiny_none_ctc_en_train/best_accuracy ``` ## 4. Inference and Deployment ### 4.1 Python Inference First, the model saved during the SVTR text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) ), you can use the following command to convert: ``` python3 tools/export_model.py -c configs/rec/rec_svtrnet.yml -o Global.pretrained_model=./rec_svtr_tiny_none_ctc_en_train/best_accuracy Global.save_inference_dir=./inference/rec_svtr_tiny_stn_en ``` **Note:** - If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the `character_dict_path` in the configuration file to the modified dictionary file. - If you modified the input size during training, please modify the `infer_shape` corresponding to SVTR in the `tools/export_model.py` file. After the conversion is successful, there are three files in the directory: ``` /inference/rec_svtr_tiny_stn_en/ ├── inference.pdiparams ├── inference.pdiparams.info └── inference.pdmodel ``` For SVTR text recognition model inference, the following commands can be executed: ``` python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_svtr_tiny_stn_en/' --rec_algorithm='SVTR' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt' ``` ![](../imgs_words_en/word_10.png) After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows: The result is as follows: ```shell Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9999998807907104) ``` ### 4.2 C++ Inference Not supported ### 4.3 Serving Not supported ### 4.4 More Not supported ## 5. FAQ 1. Since most of the operators used by `SVTR` are matrix multiplication, in the GPU environment, the speed has an advantage, but in the environment where mkldnn is enabled on the CPU, `SVTR` has no advantage over the optimized convolutional network. ## Citation ```bibtex @article{Du2022SVTR, title = {SVTR: Scene Text Recognition with a Single Visual Model}, author = {Du, Yongkun and Chen, Zhineng and Jia, Caiyan and Yin, Xiaoting and Zheng, Tianlun and Li, Chenxia and Du, Yuning and Jiang, Yu-Gang}, booktitle = {IJCAI}, year = {2022}, url = {https://arxiv.org/abs/2205.00159} } ```