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progressive rectification network for irregular text recognition

Science China(Information Sciences) 2020年第2期63卷 7-20页

作者： Yunze GAO Yingying CHEN Jinqiao WANG Hanqing LU National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences

Scene text recognition has received increasing attention in the research community. Text in the wild often possesses irregular arrangements, which typically include perspective, curved, and oriented *** of the existing methods do not work well for irregular text, especially for severely distorted text. In this paper, we propose a novel progressive rectification network(PRN) for irregular scene text recognition. Our PRN progressively rectifies the irregular text to a front-horizontal view and further boosts the recognition performance. The distortions are removed step by step by leveraging the observation that the intermediate rectified result provides good guidance for subsequent higher quality rectification. Additionally, by decomposing the rectification process into multiple procedures, the difficulty of each step is considerably ***, we specifically perform a rough rectification, and then adopt iterative refinement to gradually achieve optimal rectification. Additionally, to avoid the boundary damage problem in direct iterations, we design an envelope-refinement structure to maintain the integrity of the text during the iterative process. Instead of the rectified images, the text line envelope is tracked and continually refined, which implicitly models the transformation information. Then, the original input image is consistently utilized for transformation based on the refined envelope. In this manner, the original character information is preserved until the final transformation. These designs lead to optimal rectification to boost the performance of succeeding *** experiments on eight challenging datasets demonstrate the superiority of our method, especially on irregular benchmarks.

关键词： irregular text recognition progressive rectification iterative refinement

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