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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95092

    Title: Handwritten Numeral Recognition Based on Reduced Features Extraction and Fuzzy Membership Functions
    Authors: Jou, Chichang;Hsiao, Tai-Yuan;Lee, Hung-Chang
    Contributors: 淡江大學資訊管理學系
    Keywords: 手寫數字辨識;特徵萃取;隸屬函數;結構化分類;模糊資訊檢索;Handwritten Numeral Recognition;Feature Extraction;Membership Function;Structural Classification;Fuzzy Information Retrieval
    Date: 2002-12
    Issue Date: 2014-02-11 15:31:48 (UTC+8)
    Abstract: Structured classification has been adopted to recognize handwritten numerals by extracting feature primitives that characterize each image. We propose a handwritten numeral recognition system based on reduced features extraction and fuzzy membership functions, with the intention to find a minimal set of feature primitive without sacrificing the recognition rate. We first perform preprocessing of smoothing and thinning to obtain a skeleton for each image. For each skeleton, the following feature points are detected: terminal intersections, and directional. We then extract the following five feature primitives for each skeleton: loop, horizontal, vertical, C-like curve, and D-like curve. Two fuzzy S-functions are used as membership functions to estimate the likelihood of these primitives being close to the top and to the bottom of the image. A tree-like classifier based on the feature primitives and fuzzy membership is then applied to recognize the numerals. Handwritten numerals in NIST Special Database 19 are recognized with 88.72% correct rate.
    Relation: 二00二年國際計算機會議論文集(II)=Proceedings of the 2002 International Computer Symposium (Volume II),頁1705-1711
    Appears in Collections:[資訊管理學系暨研究所] 會議論文

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