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

文献类型：期刊论文

作者Wang, Tie-Qiang3,4; Jiang, Xiaoyi2; Liu, Cheng-Lin1,3,4
刊名PATTERN RECOGNITION
出版日期2022-03-01
卷号123页码:18
ISSN号0031-3203
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
DOI10.1016/j.patcog.2021.108416
通讯作者Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词RECOGNITION ; ALGORITHM ; SEGMENTATION ; ONLINE
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]
WOS研究方向Computer Science ; Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000802760400001
资助机构National Natural Science Foundation of China ; EU Horizon 2020 RISE Project ULTRACEPT
源URL[http://ir.ia.ac.cn/handle/173211/49512]  
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Liu, Cheng-Lin
作者单位1.CAS Ctr Excellence Brain Sci & Intelligence Techno, Beijing 100190, Peoples R China
2.Univ Munster, Dept Comp Sci, D-48149 Munster, Germany
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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