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Wavelet domain local binary pattern features for writer identification
Liang Du2; Xinge You2; Huihui Xu2; Zhifan Gao2; Yuanyan Tang1
2010-11-18
Conference Name2010 20th International Conference on Pattern Recognition
Source PublicationProceedings - International Conference on Pattern Recognition
Pages3691-3694
Conference Date23-26 Aug. 2010
Conference PlaceIstanbul, Turkey
PublisherIEEE
Abstract

The representation of writing styles is a crucial step of writer identification schemes. However, the large intra-writer variance makes it a challenging task. Thus, a good feature of writing style plays a key role in writer identification. In this paper, we present a simple and effective feature for off-line, text-independent writer identification, namely wavelet domain local binary patterns (WD-LBP). Based on WD-LBP, a writer identification algorithm is developed. WD-LBP is able to capture the essence of characteristics of writer while ignoring the variations intrinsic to every single writer. Unlike other texture framework method, we do not assign any statistical distribution assumption to the proposed method. This prevent us from making any, possibly erroneous, assumptions about the handwritten image feature distributions. The experimental results show that the proposed writer identification method achieves high accuracy of identification and outperforms recent writer identification method such as wavelet-GGD model and Gabor filtering method. 

DOIhttps://doi.org/10.1109/ICPR.2010.899
URLView the original
Language英语
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Document TypeConference paper
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Hongkong Baptist University
2.Huazhong University of Science and Technology
推荐引用方式
GB/T 7714
Liang Du,Xinge You,Huihui Xu,et al. Wavelet domain local binary pattern features for writer identification[C]:IEEE,2010:3691-3694.
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