6 November 2017 Character context: a shape descriptor for Arabic handwriting recognition
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Abstract
In the handwriting recognition field, designing good descriptors are substantial to obtain rich information of the data. However, the handwriting recognition research of a good descriptor is still an open issue due to unlimited variation in human handwriting. We introduce a “character context descriptor” that efficiently dealt with the structural characteristics of Arabic handwritten characters. First, the character image is smoothed and normalized, then the character context descriptor of 32 feature bins is built based on the proposed “distance function.” Finally, a multilayer perceptron with regularization is used as a classifier. On experimentation with a handwritten Arabic characters database, the proposed method achieved a state-of-the-art performance with recognition rate equal to 98.93% and 99.06% for the 66 and 24 classes, respectively.
© 2017 SPIE and IS&T
Mohammed Mudhsh, Mohammed Mudhsh, Rolla Almodfer, Rolla Almodfer, Pengfei Duan, Pengfei Duan, Shengwu Xiong, Shengwu Xiong, } "Character context: a shape descriptor for Arabic handwriting recognition," Journal of Electronic Imaging 26(6), 063002 (6 November 2017). https://doi.org/10.1117/1.JEI.26.6.063002 . Submission: Received: 27 July 2017; Accepted: 12 October 2017
Received: 27 July 2017; Accepted: 12 October 2017; Published: 6 November 2017
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