NDLI: Off-line Uyghur signature recognition based on modified grid information features

Content Provider	IEEE Xplore Digital Library
Author	Ubul, K. Adler, A. Abliz, G. Yasheng, M. Hamdulla, A.
Copyright Year	2012
Description	Author affiliation: Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada (Adler, A.; Yasheng, M.) \|\| School of Information Science and Engineering, Xinjiang University, 830046, Urumqi, China (Ubul, K.; Abliz, G.; Hamdulla, A.)
Abstract	Many techniques have been published on handwriting signature recognition, but none of these techniques presented are about Uyghur handwritten signature due to its complex nature. In this paper, we propose methods for off-line signature recognition for Uyghur handwriting first time. The signature images were pre-processed based on the nature of Uyghur signature. The preprocessing included noise reduction, binarization and normalization. Then multi-dimensional modified grid information features were extracted according to the character of Uyghur signature and its writing style. Finally, three kinds of classification techniques were used: Euclidean distance (ED) classifier, K nearest neighbor (K-NN) classifier and Bayes classifier. Experiments were performed using Uyghur signature samples from 50 different people with 1000 signatures. A promising result of 93.53% average correct recognition rate was achieved.
Starting Page	1056
Ending Page	1061
File Size	341198
Page Count	6
File Format	PDF
ISBN	9781467303811
e-ISBN	9781467303828
e-ISBN	9781467303804
DOI	10.1109/ISSPA.2012.6310446
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Image segmentation Handwriting recognition Databases Writing Noise Training
Content Type	Text
Resource Type	Article

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