Gradient-based approach to offline text-independent Persian writer identification

Gradient-based approach to offline text-independent Persian writer identification

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Handwritten biometric recognition (writer identification) is a process of identifying the author of a given handwriting. This process belongs to behavioural biometric systems. This study presents a gradient-based technique to offline writer identification in Persian documents. In the proposed method, some similar segmented characters were used for feature extraction. These characters were selected based on its abundance in the Persian language. Other main advantages of the proposed method included defining Persian stroke concept based on Persian characteristics, computing statistical features from Persian strokes and identifying writer by using only one stroke. The suggested method utilised gradient descriptor to extract three energy-based and eight angle-based features. This feature vector was augmented by averaging and a codebook, which utilised augmented feature vectors, was assigned to each writer for each stroke. For identification, a comparison was made of new stroke codebook with the codebook of all writers in this stroke using Kullback–Leibler distance. To test the suggested method, some characters of a standard database were manually segmented and labelled. In the meantime, a large Persian handwriting database was collected and labelled. The system was evaluated on the segmented and collected database, and displayed absolutely correct results on many of the strokes.

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