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## INTRODUCTION
PaddleOCR aims to create a rich, leading, and practical OCR tools that help users train better models and apply them into practice.
-**Recent updates**、
-- 2020.7.9 Add recognition model to support space, [recognition result](#space Chinese OCR results). For more information: [Recognition](./doc/doc_ch/recognition.md) and [quickstart](./doc/doc_ch/quickstart.md)
-- 2020.7.9 Add data auguments and learning rate decay strategies,please read [config](./doc/doc_en/config_en.md)
-- 2020.6.8 Add [dataset](./doc/doc_en/datasets_en.md) and keep updating
-- 2020.6.5 Support exporting `attention` model to `inference_model`
-- 2020.6.5 Support separate prediction and recognition, output result score
+**Live stream on coming day**: July 21, 2020 at 8 pm BiliBili station live stream
+
+**Recent updates**
+
+- 2020.7.15, Add mobile App demo , support both iOS and Android ( based on easyedge and Paddle Lite)
+- 2020.7.15, Improve the deployment ability, add the C + + inference , serving deployment. In addtion, the benchmarks of the ultra-lightweight Chinese OCR model are provided.
+- 2020.7.15, Add several related datasets, data annotation and synthesis tools.
+- 2020.7.9 Add a new model to support recognize the character "space".
+- 2020.7.9 Add the data augument and learning rate decay strategies during training.
- [more](./doc/doc_en/update_en.md)
## FEATURES
-- Lightweight Chinese OCR model, total model size is only 8.6M
+- Ultra-lightweight Chinese OCR model, total model size is only 8.6M
- Single model supports Chinese and English numbers combination recognition, vertical text recognition, long text recognition
- Detection model DB (4.1M) + recognition model CRNN (4.5M)
- Various text detection algorithms: EAST, DB
- Various text recognition algorithms: Rosetta, CRNN, STAR-Net, RARE
+- Support Linux, Windows, MacOS and other systems.
-
-### Supported Chinese models list:
-
-|Model Name|Description |Detection Model link|Recognition Model link| Support for space Recognition Model link|
-|-|-|-|-|-|
-|chinese_db_crnn_mobile|lightweight Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar) / [pre-trained model](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-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance.tar)
-|chinese_db_crnn_server|General Chinese OCR model|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/ch_models/ch_det_r50_vd_db.tar)|[inference model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar) / [pre-trained model](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-train model](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_enhance.tar)
-
-
-For testing our Chinese OCR online：https://www.paddlepaddle.org.cn/hub/scene/ocr
-
-**You can also quickly experience the lightweight Chinese OCR and General Chinese OCR models as follows:**
-
-## **LIGHTWEIGHT CHINESE OCR AND GENERAL CHINESE OCR INFERENCE**
+## Visualization
-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
-
-Please see [Quick installation](./doc/doc_en/installation_en.md)
-
-#### 2. DOWNLOAD INFERENCE MODELS
-
-#### (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*
+[More visualization](./doc/doc_en/visualization_en.md)
-Copy the detection and recognition 'inference model' address in [Chinese model List](#Supported-Chinese-model-list), download and unpack:
+You can also quickly experience the ultra-lightweight Chinese OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)
-```
-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 ..
-```
+Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in the website to obtain the QR code for installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)
-Take lightweight Chinese OCR model as an example:
+ Also, you can scan the QR code blow to install the App (**Android support only**)
-```
-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
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-
-## REFERENCES
-```
-1. EAST:
-@inproceedings{zhou2017east,
- title={EAST: an efficient and accurate scene text detector},
- author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
- booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
- pages={5551--5560},
- year={2017}
-}
-
-2. DB:
-@article{liao2019real,
- title={Real-time Scene Text Detection with Differentiable Binarization},
- author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
- journal={arXiv preprint arXiv:1911.08947},
- year={2019}
-}
-
-3. DTRB:
-@inproceedings{baek2019wrong,
- title={What is wrong with scene text recognition model comparisons? dataset and model analysis},
- author={Baek, Jeonghun and Kim, Geewook and Lee, Junyeop and Park, Sungrae and Han, Dongyoon and Yun, Sangdoo and Oh, Seong Joon and Lee, Hwalsuk},
- booktitle={Proceedings of the IEEE International Conference on Computer Vision},
- pages={4715--4723},
- year={2019}
-}
-
-4. SAST:
-@inproceedings{wang2019single,
- title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
- author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
- booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
- pages={1277--1285},
- year={2019}
-}
-
-5. SRN:
-@article{yu2020towards,
- title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
- author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
- journal={arXiv preprint arXiv:2003.12294},
- year={2020}
-}
-
-6. end2end-psl:
-@inproceedings{sun2019chinese,
- title={Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning},
- author={Sun, Yipeng and Liu, Jiaming and Liu, Wei and Han, Junyu and Ding, Errui and Liu, Jingtuo},
- booktitle={Proceedings of the IEEE International Conference on Computer Vision},
- pages={9086--9095},
- year={2019}
-}
-```
+
+## COMMUNITY
+Scan the QR code below with your wechat and completing the questionnaire, you can access to offical technical exchange group.
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