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

• URL: http://arxiv.org/abs/2109.03144v1
• Date: Tue, 7 Sep 2021 15:24:40 GMT
• Title: PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System
• Authors: Yuning Du, Chenxia Li, Ruoyu Guo, Cheng Cui, Weiwei Liu, Jun Zhou, Bin Lu, Yehua Yang, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma
• Abstract summary: We introduce bag of tricks to train a better text detector and a better text recognizer. Experiments on real data show that the precision of PP-OCRv2 is 7% higher than PP-OCR under the same inference cost.
• Score: 9.376162696601238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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