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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fei Yin Ming-Ke Zhou Qiu-Feng Wang Cheng-Lin Liu |
| Copyright Year | 2013 |
| Description | Author affiliation: Nat. Lab. of Pattern Recognition (NLPR), Inst. of Autom., Beijing, China (Fei Yin; Ming-Ke Zhou; Qiu-Feng Wang; Cheng-Lin Liu) |
| Abstract | Perturbation-based recognition is effective to recover the deformation of handwritten characters and improve the recognition performance by generating multiple distortions and selecting a distortion that best restores character deformation. Considering that the characters in a field undergo similar deformation under a consistent style, we proposed style consistent perturbation for handwritten character recognition. By generating multiple distortions for the characters in a field, each distortion style is evaluated at the field level and the uniform distortion style of maximum recognition confidence is selected to give the final result. To overcome the slight deviation from uniform style, we also propose to search the neighborhood distortions from the optimal uniform distortion for higher confidence. The experiments of handwritten Chinese character recognition on multi-writer data show that style consistent perturbation in very short fields outperforms individual character recognition, and neighborhood distortion search yields further improvement. |
| Starting Page | 1051 |
| Ending Page | 1055 |
| File Size | 368515 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769549996 |
| ISSN | 15205363 |
| DOI | 10.1109/ICDAR.2013.210 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-08-25 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Character recognition Handwriting recognition Accuracy Training Perturbation methods Writing handwritten Chinese character recognition Style consistent perturbation field classification |
| Content Type | Text |
| Resource Type | Article |
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