Knowledge Commons of Institute of Automation,CAS
Progressive rectification network for irregular text recognition | |
Gao, Yunze1,2; Chen, Yingying1; Wang, Jinqiao1; Lu, Hanqing1 | |
发表期刊 | SCIENCE CHINA-INFORMATION SCIENCES |
ISSN | 1674-733X |
2020-01-14 | |
卷号 | 63期号:2页码:1-14 |
摘要 | 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 texts. Most 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 mitigated. First, 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 recognition. Extensive 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 |
DOI | 10.1007/s11432-019-2710-7 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000514581400001 |
出版者 | SCIENCE PRESS |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/38377 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Chen, Yingying |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Gao, Yunze,Chen, Yingying,Wang, Jinqiao,et al. Progressive rectification network for irregular text recognition[J]. SCIENCE CHINA-INFORMATION SCIENCES,2020,63(2):1-14. |
APA | Gao, Yunze,Chen, Yingying,Wang, Jinqiao,&Lu, Hanqing.(2020).Progressive rectification network for irregular text recognition.SCIENCE CHINA-INFORMATION SCIENCES,63(2),1-14. |
MLA | Gao, Yunze,et al."Progressive rectification network for irregular text recognition".SCIENCE CHINA-INFORMATION SCIENCES 63.2(2020):1-14. |
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Progressive Rectific(1487KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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