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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wakahara, T. Yamashita, Y. |
| Copyright Year | 2010 |
| Abstract | This paper addresses the problem of improving the accuracy of character recognition with a limited quantity of data. The key ideas are twofold. One is distortion-tolerant template matching via hierarchical global/partial affine transformation (GAT/PAT) correlation to absorb both linear and nonlinear distortions in a parametric manner. The other is use of multiple templates per category obtained by k-means clustering in a gradient feature space for dealing with topological distortion. Recognition experiments using the handwritten numerical database IPTP CDROM1B show that the proposed method achieves a much higher recognition rate of 97.9% than that of 85.8% obtained by the conventional, simple correlation matching with a single template per category. Furthermore, comparative experiments show that the k-NN classification using the tangent distance and the GAT correlation technique achieves recognition rates of 97.5% and 98.7%, respectively. |
| Starting Page | 2873 |
| Ending Page | 2876 |
| File Size | 1519371 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424475421 |
| ISSN | 10514651 |
| e-ISBN | 9781424475414 |
| DOI | 10.1109/ICPR.2010.704 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Correlation Nonlinear distortion Character recognition Handwriting recognition Accuracy Pixel character recognition distortion-tolerant matching global/partial affine transformation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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