For testing our Chinese OCR online：https://www.paddlepaddle.org.cn/hub/scene/ocr
@ -34,7 +35,7 @@ For testing our Chinese OCR online：https://www.paddlepaddle.org.cn/hub/scene/o
The picture above is the result of our lightweight Chinese OCR model. For more testing results, please see the end of the article [lightweight Chinese OCR results](#lightweight-Chinese-OCR-results) and [General Chinese OCR results](#General-Chinese-OCR-results).
The picture above is the result of our lightweight Chinese OCR model. For more testing results, please see the end of the article [lightweight Chinese OCR results](#lightweight-Chinese-OCR-results) , [General Chinese OCR results](#General-Chinese-OCR-results) and [Support for space Recognition Model](#Space-Chinese-OCR-results).
#### 1. ENVIRONMENT CONFIGURATION
@ -45,22 +46,42 @@ Please see [Quick installation](./doc/doc_en/installation_en.md)
#### (1) Download 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*
Copy the detection and recognition 'inference model' address in [Chinese model List](#Supported-Chinese-model-list), download and unpack:
```
mkdir inference && cd inference
# Download the detection part of the Chinese OCR and decompress it
wget {url/of/detection/inference_model} && tar xf {name/of/detection/inference_model/package}
# Download the recognition part of the Chinese OCR and decompress it
wget {url/of/recognition/inference_model} && tar xf {name/of/recognition/inference_model/package}
cd ..
```
Take lightweight Chinese OCR model as an example:
```
mkdir inference && cd inference
# Download the detection part of the lightweight Chinese OCR and decompress it
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 lightweight Chinese OCR and decompress it
wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
# Download the space-recognized part of the lightweight Chinese OCR and decompress it
wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar && tar xf ch_rec_mv3_crnn_enhance_infer.tar
cd ..
```
#### (2) Download General Chinese OCR models
After the decompression is completed, the file structure should be as follows:
```
mkdir inference && cd inference
# Download the detection part of the general Chinese OCR model and decompress it
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
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 ..
|-inference
|-ch_rec_mv3_crnn
|- model
|- params
|-ch_det_mv3_db
|- model
|- params
...
```
#### 3. SINGLE IMAGE AND BATCH PREDICTION
@ -85,6 +106,13 @@ To run inference of the Generic Chinese OCR model, follow these steps above to d
To run inference of the space-Generic Chinese OCR model, follow these steps above to download the corresponding models and update the relevant parameters. Examples are as follows:
```
# Prediction on a single image by specifying image path to image_dir
For more text detection and recognition models, please refer to the document [Inference](./doc/doc_en/inference_en.md)
## DOCUMENTATION
@ -147,15 +175,15 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-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:
|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)|
|lightweight Chinese model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.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)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance_infer.tar) & [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)|
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
| loss_type | Set loss type | ctc | Supports two types of loss: ctc / attention |
| distort | Set use distort | false | Support distort type ,read [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) |
| use_space_char | Wether to recognize space | false | Only support in character_type=ch mode |
| reader_yml | Set the reader configuration file | ./configs/rec/rec_icdar15_reader.yml | \ |
xiaoting
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