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Integration of Structural and Statistical Information for Unconstrained Handwritten Numeral Recognition

Cai, Jinhai & Liu, Zhi-Qiang (1999) Integration of Structural and Statistical Information for Unconstrained Handwritten Numeral Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(3), pp. 263-270.

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Abstract

In this paper, we propose an approach that integrates the statistical and structural information for unconstrained handwritten numeral recognition. This approach uses state-duration adapted transition probability to improve the modeling of state-duration in conventional HMMs and uses macro-states to overcome the difficulty in modeling pattern structures by HMMs. The proposed method is superior to conventional approaches in many aspects. In the statistical and structural models, the orientations are encoded into discrete codebooks and the distributions of locations are modeled by joint Gaussian distribution functions. The experimental results show that the proposed approach can achieve high performance in terms of speed and accuracy.

Impact and interest:

59 citations in Scopus
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43 citations in Web of Science®

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440 since deposited on 24 Mar 2006
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ID Code: 3777
Item Type: Journal Article
Additional Information: Jinhai was affiliated with the University of Melbourne at the time of publication.
Keywords: Handwritten numeral recognition, Hidden Markov model, Structural model, Hybird classifiers, Outer contours, Chain code, based features, Macro state
DOI: 10.1109/34.754622
ISSN: 0162-8828
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 1999 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 24 Mar 2006
Last Modified: 09 Jun 2010 22:31

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