Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Towards open-set text recognition via label-to-prototype learning | |
Liu, Chang1; Yang, Chun1; Qin, Hai-Bo1; Zhu, Xiaobin1; Liu, Cheng-Lin2 ; Yin, Xu-Cheng1
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| Source Publication | PATTERN RECOGNITION
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| ISSN | 0031-3203 |
| 2023-02-01 | |
| Volume | 134Pages:13 |
| Corresponding Author | Yang, Chun(chunyang@ustb.edu.cn) ; Yin, Xu-Cheng(xuchengyin@ustb.edu.cn) |
| Abstract | 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foreign languages can be expensive, whereas retraining the model each time when a "novel" character is discovered from historical documents costs both time and resources.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. |
| Keyword | Open-set recognition Scene text recognition Low-shot recognition |
| DOI | 10.1016/j.patcog.2022.109109 |
| WOS Keyword | NETWORK ; CLASSIFICATION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | National Key Research and Development Program of China ; National Science Fund for Distinguished Young Scholars ; National Natural Science Foundation of China |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000880031000003 |
| Publisher | ELSEVIER SCI LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50700 |
| Collection | 模式识别国家重点实验室_模式分析与学习 |
| Corresponding Author | Yang, Chun; Yin, Xu-Cheng |
| Affiliation | 1.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Dept Comp Sci & Technol, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation 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. |
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