Query Pixel Guided Stroke Extraction with Model-Based Matching for Offline Handwritten Chinese Characters

doi:10.1016/j.patcog.2021.108416

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Query Pixel Guided Stroke Extraction with Model-Based Matching for Offline Handwritten Chinese Characters
	Wang, Tie-Qiang1,2 ; Jiang, Xiaoyi 3; Liu, Cheng-Lin1,2,4
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2022-03-01
卷号	123 页码:18
通讯作者	Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
摘要	Stroke extraction and matching are critical for structural interpretation based applications of handwrit-ten Chinese characters, such as Chinese character education and calligraphy analysis. Stroke extraction from offline handwritten Chinese characters is difficult because of the missing of temporal information, the multi-stroke structures and the distortion of handwritten shapes. In this paper, we propose a compre-hensive scheme for solving the stroke extraction problem for handwritten Chinese characters. The method consists of three main steps: (1) fully convolutional network (FCN) based skeletonization; (2) query pixel guided stroke extraction; (3) model-based stroke matching. Specifically, based on a recently proposed ar-chitecture of FCN, both the stroke skeletons and cross regions are firstly extracted from the character image by the proposed SkeNet and CrossNet, respectively. Stroke extraction is solved by simulating the human perception that once given a certain pixel from non-cross region of a stroke, the whole stroke containing the pixel can be traced. To realize this idea, we formulate stroke extraction as a problem of pairing and connecting skeleton-wise stroke segments which are adjacent to the same cross region, where the pairing consistency between stroke segments is measured using a PathNet [1]. To reduce the ambiguity of stroke extraction, the extracted candidate strokes are matched with a character model con-sisting of standard strokes by tree search to identify the correct strokes. For verifying the effectiveness of the proposed method, we train and test our models on character images with stroke segmentation an-notations generated from the online handwriting datasets CASIA-OLHWDB and ICDAR13-Online, as well as a dataset of Regularly-Written online handwritten characters (RW-OLHWDB). The experimental results demonstrate the effectiveness of the proposed method and provide several benchmarks. Particularly, the precisions of stroke extraction for ICDAR13-Online and RW-OLHWDB are 89.0% and 94.9%, respectively.(c) 2021 Elsevier Ltd. All rights reserved.
关键词	Stroke extraction Conditional fully convolutional network PathNet Stroke matching Tree search Stroke extraction Conditional fully convolutional network PathNet Stroke matching Tree search
DOI	10.1016/j.patcog.2021.108416
关键词[WOS]	RECOGNITION ; ALGORITHM ; SEGMENTATION ; ONLINE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61836014] ; National Natural Science Foundation of China[61721004] ; EU Horizon 2020 RISE Project ULTRACEPT[778062]
项目资助者	National Natural Science Foundation of China ; EU Horizon 2020 RISE Project ULTRACEPT
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000802760400001
出版者	ELSEVIER SCI LTD
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49513
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Liu, Cheng-Lin
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Univ Munster, Dept Comp Sci, D-48149 Munster, Germany 4.CAS Ctr Excellence Brain Sci & Intelligence Techno, Beijing 100190, 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	Wang, Tie-Qiang,Jiang, Xiaoyi,Liu, Cheng-Lin. Query Pixel Guided Stroke Extraction with Model-Based Matching for Offline Handwritten Chinese Characters[J]. PATTERN RECOGNITION,2022,123:18.
APA	Wang, Tie-Qiang,Jiang, Xiaoyi,&Liu, Cheng-Lin.(2022).Query Pixel Guided Stroke Extraction with Model-Based Matching for Offline Handwritten Chinese Characters.PATTERN RECOGNITION,123,18.
MLA	Wang, Tie-Qiang,et al."Query Pixel Guided Stroke Extraction with Model-Based Matching for Offline Handwritten Chinese Characters".PATTERN RECOGNITION 123(2022):18.