PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System

• Main Authors: Du, Yuning, Li, Chenxia, Guo, Ruoyu, Cui, Cheng, Liu, Weiwei, Zhou, Jun, Lu, Bin, Yang, Yehua, Liu, Qiwen, Hu, Xiaoguang, Yu, Dianhai, Ma, Yanjun
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2021
• Subjects: Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; DML
• Online Access: https://dx.doi.org/10.48550/arxiv.2109.03144; https://arxiv.org/abs/2109.03144

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. : 8 pages, 9 figures, 5 tables