  Optical Character Recognition (OCR) systems have been widely used in various
of application scenarios. Designing an OCR system is still a challenging task.
In previous work, we proposed a practical ultra lightweight OCR system (PP-OCR)
to balance the accuracy against the efficiency. In order to improve the
accuracy of PP-OCR and keep high efficiency, in this paper, we propose a more
robust OCR system, i.e. PP-OCRv2. We introduce bag of tricks to train a better
text detector and a better text recognizer, which include Collaborative Mutual
Learning (CML), CopyPaste, Lightweight CPUNetwork (LCNet), Unified-Deep Mutual
Learning (U-DML) and Enhanced CTCLoss. Experiments on real data show that the
precision of PP-OCRv2 is 7% higher than PP-OCR under the same inference cost.
It is also comparable to the server models of the PP-OCR which uses ResNet
series as backbones. All of the above mentioned models are open-sourced and the
code is available in the GitHub repository PaddleOCR which is powered by
PaddlePaddle.
