For testing our Chinese OCR online：https://www.paddlepaddle.org.cn/hub/scene/ocr
**也可以按如下教程快速体验超轻量级中文OCR和通用中文OCR模型。**
**You can also quickly experience the Ultra-lightweight Chinese OCR and General Chinese OCR models as follows:**
## **Ultra-lightweight Chinese OCR and General Chinese OCR inference**
## **超轻量级中文OCR以及通用中文OCR体验**
The picture above is the result of our Ultra-lightweight Chinese OCR model. For more testing results, please see the end of the article [Ultra-lightweight Chinese OCR results](#Ultra-lightweight-Chinese-OCR-results) and [General Chinese OCR results](#General-Chinese-OCR-results).
#### (1) Download Ultra-lightweight Chinese OCR models
*If wget is not installed in the windows system, you can copy the link to the browser to download the model. After model downloaded, unzip it and place it in the corresponding directory*
#### (1)超轻量级中文OCR模型下载
```
mkdir inference && cd inference
# Download the detection part of the Ultra-lightweight Chinese OCR and decompress it
# 下载超轻量级中文OCR模型的检测模型并解压
wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
# Download the recognition part of the Ultra-lightweight Chinese OCR and decompress it
# 下载超轻量级中文OCR模型的识别模型并解压
wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
cd ..
```
#### (2) Download General Chinese OCR models
#### (2)通用中文OCR模型下载
```
mkdir inference && cd inference
# Download the detection part of the general Chinese OCR model and decompress it
# 下载通用中文OCR模型的检测模型并解压
wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar && tar xf ch_det_r50_vd_db_infer.tar
# Download the recognition part of the generic Chinese OCR model and decompress it
# 下载通用中文OCR模型的识别模型并解压
wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar && tar xf ch_rec_r34_vd_crnn_infer.tar
cd ..
```
#### 3. Single image and batch image prediction
#### 3.单张图像或者图像集合预测
The following code implements text detection and recognition inference tandemly. When performing prediction, you need to specify the path of a single image or image folder through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detection model, and the parameter `rec_model_dir` specifies the path to the recognition model. The visual prediction results are saved to the `./inference_results` folder by default.
To run inference of the Generic Chinese OCR model, follow these steps above to download the corresponding models and update the relevant parameters. Examples are as follows:
通用中文OCR模型的体验可以按照上述步骤下载相应的模型，并且更新相关的参数，示例如下：
```
# Prediction on a single image by specifying image path to image_dir
For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/datasets.md#1icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for Chinese detection task are as follows:
*Note: For the training and evaluation of the above DB model, post-processing parameters box_thresh=0.6 and unclip_ratio=1.5 need to be set. If using different datasets and different models for training, these two parameters can be adjusted for better result.
For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/detection.md)
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/datasets.md#1icdar2019-lsvt) dataset and cropout 30w traning data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the Chinese model. The related configuration and pre-trained models are as follows:
|Ultra-lightweight Chinese model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|
|General Chinese OCR model|Resnet34_vd|rec_chinese_common_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)|
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/recognition.md)
1. Prediction error：got an unexpected keyword argument 'gradient_clip'
The installed paddle version is not correct. At present, this project only supports paddle1.7, which will be adapted to 1.8 in the near future.
2. Error when using attention-based recognition model: KeyError: 'predict'
1.**预测报错：got an unexpected keyword argument 'gradient_clip'**
安装的paddle版本不对，目前本项目仅支持paddle1.7，近期会适配到1.8。
The inference of recognition model based on attention loss is still being debugged. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss first. In practice, it is also found that the recognition model based on attention loss is not as effective as the one based on CTC loss.
When there are a lot of texts in the picture, the prediction time will increase. You can use `--rec_batch_num` to set a smaller prediction batch size. The default value is 30, which can be changed to 10 or other values.
It is expected that the service deployment based on Serving and the mobile deployment based on Paddle Lite will be released successively in mid-to-late June. Stay tuned for more updates.
5. Release time of self-developed algorithm
Baidu Self-developed algorithms such as SAST, SRN and end2end PSL will be released in June or July. Please be patient.
5.**自研算法发布时间**
自研算法SAST、SRN、End2End-PSL都将在6-7月陆续发布，敬请期待。
[more](./doc/FAQ.md)
## Welcome to the PaddleOCR technical exchange group
Add Wechat: paddlehelp, remark OCR, small assistant will pull you into the group ~
## 欢迎加入PaddleOCR技术交流群
加微信：paddlehelp，备注OCR，小助手拉你进群～
## References
## 参考文献
```
1. EAST:
@inproceedings{zhou2017east,
...
...
@@ -255,8 +249,8 @@ Add Wechat: paddlehelp, remark OCR, small assistant will pull you into the group
}
```
## License
This project is released under <ahref="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
