文献类型：期刊文献

英文题名：Optical character correction of large-curvature annular sector text in polar coordinate system

作者：Wang, Ruiping[1,2];Cao, Wei[3];Wu, Shihong[2];Jia, Meng[4];Wang, Xiaoping[1]

第一作者：Wang, Ruiping

通讯作者：Wang, RP[1];Wang, XP[1];Wang, RP[2]

机构：[1]Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automation, Wuhan 430074, Peoples R China;[2]YGSOFT INC, Res Inst Yuanguang, Zhuhai 519085, Peoples R China;[3]Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China;[4]Xinxiang Univ, Sch Mech & Elect Engn, Xinxiang 453003, Peoples R China

第一机构：Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automation, Wuhan 430074, Peoples R China

通讯机构：[1]corresponding author), Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automation, Wuhan 430074, Peoples R China;[2]corresponding author), YGSOFT INC, Res Inst Yuanguang, Zhuhai 519085, Peoples R China.

年份：2023

卷号：167

起止页码：157-163

外文期刊名：PATTERN RECOGNITION LETTERS

收录：;EI(收录号：20230813616769);Scopus(收录号：2-s2.0-85148332690);WOS:【SCI-EXPANDED(收录号:WOS:000944879600001)】；

基金：This work is supported by the National Natural Science Foundation of China under Grant nos. 61876209 , 61936004 , Henan University Science and Technology Innovation Talent Project under Grant nos. 21HASTIT021, and Xinxiang City Major Science and Technology Project under Grant nos. 21ZD009.

语种：英文

外文关键词：Optical character correction; Large curvature; Annular sector text; Polar coordinate transformation; OCR

摘要：Optical character recognition (OCR) of complex morphologies represented by large-curvature annular sector text (AST) is a very challenging task. A three-segment text recognition framework consisting of detection, correction and recognition is currently an effective method for dealing with complex morphological OCR. Optical character correction (OCC) is a key component in processing largecurvature AST. This paper proposes an OCC method in the polar coordinate system, which consists of control point preprocessing, polar coordinate transformation, and image remapping. The control point preprocessing is used to normalize the control points of the large curvature AST region; the polar coordinate transformation is to convert the pixels in the rectangular coordinate system to the polar coordinate system; image remapping maps the original image in polar coordinate system to polar coordinate space for re-representation. The method proposed in this paper can be used in conjunction with most detection and recognition modules and is applicable to any language type. Furthermore, the correction process consumes very little computational resources and has little impact on the speed of text detection and recognition. Experimental results show that the proposed method outperforms state-of-the-art algorithms in large curvature AST correction and recognition experiments