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arxiv: 2109.03144 · v2 · pith:3POX4UYOnew · submitted 2021-09-07 · 💻 cs.CV

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

Yuning Du , Chenxia Li , Ruoyu Guo , Cheng Cui , Weiwei Liu , Jun Zhou , Bin Lu , Yehua Yang
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Qiwen Liu Xiaoguang Hu Dianhai Yu Yanjun Ma
This is my paper
classification 💻 cs.CV
keywords pp-ocrsystemlightweightpp-ocrv2accuracybetterefficiencylearning
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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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