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

• Published in: IET biometrics 2019-03, Vol.8 (2), p.144-149
• Main Authors: Alavi Gharahbagh, Abdorreza, Yaghmaee, Farzin
• Format: Article
• Language: English
• Subjects: angle‐based features; behavioural biometric systems; Biometrics; biometrics (access control); Classification; energy‐based; Feature extraction; feature vector; Genetic algorithms; given handwriting; gradient descriptor; gradient methods; gradient‐based approach; gradient‐based technique; Handwriting; Handwriting recognition; handwritten biometric recognition; Identification; identifying writer; image segmentation; Interactive computer systems; Methods; Neural networks; Persian characteristics; Persian documents; Persian handwriting database; Persian language; Persian stroke concept; Persian strokes; Probability distribution; Research Article; segmented collected database; similar segmented characters; statistical features; stroke codebook; suggested method; Support vector machines; text analysis; text‐independent Persian writer identification; Wavelet transforms; Writers
• ISSN: 2047-4938; 2047-4946; 2047-4946
• DOI: 10.1049/iet-bmt.2018.5117

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.