Handwritten Numeral Recognition Based on Simplified Feature Extraction, Structural Classification and Fuzzy Memberships

doi:10.1007/978-3-540-24677-0_39

淡江大學機構典藏 > 商管學院 > 資訊管理學系暨研究所 > 期刊論文 > Item 987654321/68610

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题名:	Handwritten Numeral Recognition Based on Simplified Feature Extraction, Structural Classification and Fuzzy Memberships
作者:	Jou, Chi-chang;Lee, Hung-chang
贡献者:	淡江大學資訊管理學系
日期:	2004-05
上传时间:	2011-10-23 13:19:13 (UTC+8)
摘要:	Structural classification recognizes handwritten numerals by extracting geometric primitives that characterize each image. We propose a handwritten numeral recognition system based on simplified feature extraction, structural classification and fuzzy memberships, with the intention to find a small set of primitives without sacrificing the recognition rate. For each image, we first perform simplified preprocessing of smoothing and thinning to obtain a skeleton. For each skeleton, the following feature points are detected: terminal, intersection, and directional. We then extract the following primitives for each skeleton: loop, horizontal, vertical, leftward curve, and rightward curve. A fuzzy S-function is used as the membership function to estimate the likelihood of these primitives being close to the vertical boundary of the image. A tree-like classifier based on the extracted feature points, primitives and fuzzy memberships is then applied to recognize the numerals. Handwritten numerals in NIST Special Database 19 are recognized with correct rate between 87.33% and 88.72%.
關聯:	Lecture notes in artificial intelligence 3029,　p.372-381
DOI:	10.1007/978-3-540-24677-0_39
显示于类别:	[資訊管理學系暨研究所] 期刊論文

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