Integration of Structural and Statistical Information for Unconstrained Handwritten Numeral Recognition
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Description
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.
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| ID Code: | 3777 |
|---|---|
| Item Type: | Contribution to Journal (Journal Article) |
| Refereed: | Yes |
| 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 |
| Pure ID: | 60077772 |
| Divisions: | Past > QUT Faculties & Divisions > Faculty of Science and Technology Past > Research Centres > CRC for Diagnostics |
| 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 00:00 |
| Last Modified: | 03 Mar 2024 18:31 |
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