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Towards open-set text recognition via label-to-prototype learning

文献类型：期刊论文

作者Liu, Chang2; Yang, Chun2; Qin, Hai-Bo2; Zhu, Xiaobin2; Liu, Cheng-Lin1; Yin, Xu-Cheng2
刊名PATTERN RECOGNITION
出版日期2023-02-01
卷号134页码:13
ISSN号0031-3203
关键词Open-set recognition Scene text recognition Low-shot recognition
DOI10.1016/j.patcog.2022.109109
通讯作者Yang, Chun(chunyang@ustb.edu.cn) ; Yin, Xu-Cheng(xuchengyin@ustb.edu.cn)
英文摘要Scene text recognition is a popular research topic which is also extensively utilized in the industry. Al-though many methods have achieved satisfactory performance for the close-set text recognition chal-lenges, these methods lose feasibility in open-set scenarios, where collecting data or retraining models for novel characters could yield a high cost. For example, annotating samples for foreign languages can be expensive, whereas retraining the model each time when a "novel" character is discovered from historical documents costs both time and resources. In this paper, we introduce and formulate a new open-set text recognition task which demands the capability to spot and recognize novel characters without retrain-ing. A label-to-prototype learning framework is also proposed as a baseline for the new task. Specifically, the framework introduces a generalizable label-to-prototype mapping function to build prototypes (class centers) for both seen and unseen classes. An open-set predictor is then utilized to recognize or reject samples according to the prototypes. The implementation of rejection capability over out-of-set charac-ters allows automatic spotting of unknown characters in the incoming data stream. Extensive experiments show that our method achieves promising performance on a variety of zero-shot, close-set, and open-set text recognition datasets. (c) 2022 Elsevier Ltd. All rights reserved.
WOS关键词NETWORK ; CLASSIFICATION
资助项目National Key Research and Development Program of China ; National Science Fund for Distinguished Young Scholars ; National Natural Science Foundation of China ; [2020AAA09701] ; [62125601] ; [62006018] ; [62076024]
WOS研究方向Computer Science ; Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000880031000003
资助机构National Key Research and Development Program of China ; National Science Fund for Distinguished Young Scholars ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/50699]  
专题自动化研究所_模式识别国家重点实验室_模式分析与学习团队
通讯作者Yang, Chun; Yin, Xu-Cheng
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Dept Comp Sci & Technol, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Chang,Yang, Chun,Qin, Hai-Bo,et al. Towards open-set text recognition via label-to-prototype learning[J]. PATTERN RECOGNITION,2023,134:13.
APA Liu, Chang,Yang, Chun,Qin, Hai-Bo,Zhu, Xiaobin,Liu, Cheng-Lin,&Yin, Xu-Cheng.(2023).Towards open-set text recognition via label-to-prototype learning.PATTERN RECOGNITION,134,13.
MLA Liu, Chang,et al."Towards open-set text recognition via label-to-prototype learning".PATTERN RECOGNITION 134(2023):13.

入库方式： OAI收割

来源：自动化研究所

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