The Optical Character Recognition (OCR) systems have been widely used in
various of application scenarios, such as office automation (OA) systems,
factory automations, online educations, map productions etc. However, OCR is
still a challenging task due to the various of text appearances and the demand
of computational efficiency. In this paper, we propose a practical ultra
lightweight OCR system, i.e., PP-OCR. The overall model size of the PP-OCR is
only 3.5M for recognizing 6622 Chinese characters and 2.8M for recognizing 63
alphanumeric symbols, respectively. We introduce a bag of strategies to either
enhance the model ability or reduce the model size. The corresponding ablation
experiments with the real data are also provided. Meanwhile, several
pre-trained models for the Chinese and English recognition are released,
including a text detector (97K images are used), a direction classifier (600K
images are used) as well as a text recognizer (17.9M images are used). Besides,
the proposed PP-OCR are also verified in several other language recognition
tasks, including French, Korean, Japanese and German. All of the above
mentioned models are open-sourced and the codes are available in the GitHub
repository, i.e., this https URL

本论文提出了 PP-OCR，一个高效的光学字符识别系统，其模型大小仅有 3.5M。该系统通过采用一系列优化策略来提高模型能力和减小模型大小。此外，该系统支持多语言识别，包括法语、韩语、日语和德语。