Paddleocr

**Release Note**

1. Newly release model pruning and model quantization tools based on PaddleSlim. [Path](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.1/deploy/slim)
2. Newly release mobile deployment tools based on Paddle-Lite. [Path](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.1/deploy/lite)
3. Newly release Android demo of ppocr system. [path](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.1/deploy/android_demo)
4. Newly release service deployment based on Paddle Serving. [path](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.1/deploy/pdserving)

**Release Note**

1. Newly release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md).
2. Newly release multi language recognition model, support more than 80 languages recognition. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md).
3. Optimize the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English).

**Release Note**

一、Support dynamic graph programming paradigm, adapted to Paddle 2.0, including:
1. Detection algorithm: DB, EAST, SAST
2. Recognition algorithm: Rosetta, CRNN, RARE, SRN, STAR-Net
3. PPOCR Chinese models：
(1) Detection models: mobile, server
(2) Text direction classification models: mobile
(3) Recognition models: mobile, server
4. Multilingual models:
(1) English: mobile
(2) Japanese, Korean, French, German, etc. 25 languages in total: mobile

二、The related works on deployment have been well adapted, including Inference(Python, C++) , whl, and serving

三、Release the annotation and synthesis tools:
1. Release a new data synthesis tool, i.e., Style-Text，easy to synthesize a large number of images which are similar to the target scene image.
2. Release a new data annotation tool, i.e., PPOCRLabel, which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.

3.5M practical ultra lightweight OCR system, support training and deployment among server, mobile, embedded and IoT devices