A novel stroke-based feature extraction for handwritten Chinese character recognition

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > journal & Dissertation > Item 987654321/29138

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29138

Title:	A novel stroke-based feature extraction for handwritten Chinese character recognition
Authors:	Chiu,HP;Tseng,DC
Contributors:	資訊工程研究所
Keywords:	THINNING DIGITAL PATTERNS;FAST PARALLEL ALGORITHM;NEURAL NETWORKS;IMAGES;CLASSIFICATION;TRANSLATION;ROTATION;SYSTEM
Date:	1999
Issue Date:	2010-06-29 20:14:37 (UTC+8)
Publisher:	中央大學
Abstract:	A stroke-based approach to extract skeletons and structural features for handwritten Chinese character recognition is proposed. We first determine stroke directions based on the directional run-length information of binary character patterns. According to the stroke directions and their adjacent relationships, we split strokes into stroke and fork segments, and then extract the skeletons of the stroke segments called skeleton segments. After all skeleton segments are extracted, fork segments are processed to find the fork points and fork degrees. Skeleton segments that touch a fork segment are connected at the fork point, and all connected skeleton segments form the character skeleton. According to the extracted skeletons and fork points, we can extract primitive strokes and stroke direction maps for recognition. A simple classifier based on the stroke direction map is presented to recognize regular and rotated characters to verify the ability of the proposed feature extraction for handwritten Chinese character recognition. Several experiments are carried out, and the experimental results show that the proposed approach can easily and effectively extract skeletons and structural features, and works well for handwritten Chinese character recognition. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Relation:	PATTERN RECOGNITION
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] journal & Dissertation

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