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논문 기본 정보

자료유형
학술저널
저자정보
Sharada Laxman Kore (Bharati Vidyapeeth Deemed University) Shaila Dinkar Apte (Rajarshi Shahu College of Engineering)
저널정보
Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.10 No.2
발행연도
2016.6
수록면
39 - 50 (12page)

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In this paper, we present a comparative study of spatial domain features for writer identification and verification with different ink width conditions. The existing methods give high error rates, when comparing two handwritten images with different pen types. To the best of our knowledge, we are the first to design the feature with different ink width conditions. To address this problem, contour based features were extracted using a chain code method. To improve accuracy at higher levels, we considered histograms of chain code and variance in bins of histogram of chain code as features to discriminate handwriting samples. The system was trained and tested for 1,000 writers with two samples using different writing instruments. The feature performance is tested on our newly created dataset of 4,000 samples. The experimental results show that the histogram of chain code feature is good compared to other methods with false acceptance rate of 11.67%, false rejection rate of 36.70%, average error rates of 24.18%, and average verification accuracy of 75.89% on our new dataset. We also studied the effect of amount of text and dataset size on verification accuracy.

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Abstract
Ⅰ. INTRODUCTION
Ⅱ. DATASET
Ⅲ. FEATURE EXTRACTION
Ⅳ. WRITER VERIFICATION
Ⅴ. EXPERIMENTAL RESULTS AND DISCUSSIONS
Ⅵ. CONCLUSIONS AND FEATURE SCOPE
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