New hybrid arabic handwriting recognizer
| dc.contributor.author | Chergui, Leila | |
| dc.contributor.author | Kef, Maâmar | |
| dc.contributor.author | Chikhi, Salim | |
| dc.date.accessioned | 2022-04-27T04:53:43Z | |
| dc.date.available | 2022-04-27T04:53:43Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | Recently, there is a popular belief that classifier combination of different architecture could complement each other for improving results performance. In this paper we introduce a framework to combine results of multiple classifiers for offline Arabic handwriting recognition, by introducing a new scheme of combination of Multi Layer Perceptron and ART1 networks. Besides using two different recognition architectures (MLP and ART1 networks), we exploit various feature sets calculated from the contour of image; the Hu moments and features obtained with sliding windows. The implementation results on IFN/ENIT database show a high degree of accuracy by applying the majority vote method. | ar |
| dc.identifier.isbn | 978-1-4673-1658-3/12 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/13003 | |
| dc.language.iso | en | ar |
| dc.publisher | IEEE | ar |
| dc.subject | ART1 network | ar |
| dc.subject | Combining classifier | ar |
| dc.subject | Hu moment | ar |
| dc.subject | Multi layer perceptron | ar |
| dc.title | New hybrid arabic handwriting recognizer | ar |
| dc.type | Article | ar |
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