Large-scale continual learning for ancient Chinese character recognition

doi:10.1016/j.patcog.2024.110283

	Large-scale continual learning for ancient Chinese character recognition
	Xu, Yue1,2 ; Zhang, Xu-Yao1,2 ; Zhang, Zhaoxiang1,2 ; Liu, Cheng-Lin1,2
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2024-06-01
卷号	150 页码:15
通讯作者	Zhang, Xu-Yao(xyz@nlpr.ia.ac.cn)
摘要	Ancient Chinese character recognition is a challenging problem in the field of pattern recognition. It is difficult to collect all character classes during the training stage due to the numerous classes of ancient Chinese characters and the likelihood of discovering new characters over time. A solution to address this problem is continual learning. However, most continual learning methods are not well -suited for large-scale applications, making them insufficient for solving the problem of ancient Chinese character recognition. Although saving raw data for old classes is a good approach for continual learning to address large-scale problems, it is often infeasible due to the lack of data accessibility in reality. To solve these problems, we propose a large-scale continual learning framework based on the convolutional prototype network (CPN), which does not save raw data for old classes. In this paper, several basic strategies have been proposed for the initial training stage to enhance the feature extraction ability and robustness of the network, which can improve the performance of the model in continual learning. In addition, we propose two practical methods in varying feature space (parameters of feature extractor are changeable) and fixed feature space (parameters of feature extractor are fixed), which enable the model to carry out large-scale continual learning. The proposed method does not save the raw data of old classes and enables simultaneous classification of all existing classes without knowing the incremental batch number. Experiments on the CASIA-AHCDB dataset with 5000 character classes demonstrate the effectiveness and superiority of the proposed method.
关键词	Continual learning Class-incremental learning Convolutional prototype network Character recognition Ancient Chinese characters
DOI	10.1016/j.patcog.2024.110283
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001176417300001
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/56973
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhang, Xu-Yao
作者单位	1.Chinese Acad Sci, State Key Lab Multimodal Artificial Intelligence S, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Xu, Yue,Zhang, Xu-Yao,Zhang, Zhaoxiang,et al. Large-scale continual learning for ancient Chinese character recognition[J]. PATTERN RECOGNITION,2024,150:15.
APA	Xu, Yue,Zhang, Xu-Yao,Zhang, Zhaoxiang,&Liu, Cheng-Lin.(2024).Large-scale continual learning for ancient Chinese character recognition.PATTERN RECOGNITION,150,15.
MLA	Xu, Yue,et al."Large-scale continual learning for ancient Chinese character recognition".PATTERN RECOGNITION 150(2024):15.