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A Haar Wavelet-Based Zoning For Offline Arabic Handwritten Character Recognition | ||
| Journal of University of Babylon | ||
| Article 1, Volume 23, Issue 2, 2015, Pages 575-585 | ||
| Author | ||
| Farah Mohammed Hassan AL-Shareefi | ||
| Abstract | ||
| Due to the nature of handwriting with high degree of variability and imprecision, obtaining features that represent characters is a difficult task. In this research, a features extraction method for handwritten Arabic Character recognition is investigated. Its major goal is to maximize the recognition rate with the least amount of elements. This method compute the 1 level Haar Wavelet Transform for Binary character image, then divide the Wavelet space into 8 Zones, for each Zone, three features have been extracted: mean, standard division, and skewness. The Recognition have been done using Mahalanobis distance. The proposed method provides good recognition accuracy of 73% for handwritten characters even with fewer train samples | ||
| Keywords | ||
| Haar Wavelet; Zoning; Mahalanobis Distance; Skewness | ||
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