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- W2741233312 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. Keyword: Haar Wavelet, Zoning, Mahalanobis Distance, Skewness." @default.
- W2741233312 created "2017-08-08" @default.
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- W2741233312 date "2015-01-01" @default.
- W2741233312 modified "2023-09-24" @default.
- W2741233312 title "A Haar Wavelet-Based Zoning For Offline Arabic Handwritten Character Recognition" @default.
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