淡江大學機構典藏:Item 987654321/21320
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/21320


    Title: Handwritten Numeral Recognition Based on Simplified Feature Extraction, Structural Classification and Fuzzy Memberships
    Authors: Jou, Chi-chang;Lee, Hung-chang
    Contributors: 淡江大學資訊管理學系
    Date: 2004-04-22
    Issue Date: 2009-11-30 13:22:38 (UTC+8)
    Publisher: Berlin Heidelberg: Springer-Verlag GmbH.
    Abstract: 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%.
    Relation: Lecture Notes in Computer Science 3029: Innovations in Applied Artificial Intelligence,　p.372-381
    DOI: 10.1007/978-3-540-24677-0_39
    Appears in Collections:[Graduate Institute & Department of Information Management] Chapter

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