Multi-script off-line signature identification

In this paper, we present an empirical contribution towards the understanding of multi-script signature identification. In the proposed signature identification system, the signatures of Bengali (Bangla), Hindi (Devanagari) and English are considered for the identification process. This system will identify whether a claimed signature belongs to the group of Bengali, Hindi or English signatures. Zernike Moment and histogram of gradient are employed as two different feature extraction techniques. In the proposed system, Support Vector Machines (SVMs) are considered as classifiers for signature identification. A database of 2100 Bangla signatures, 2100 Hindi signatures and 2100 English signatures are used for experimentation. Two different results based on two different feature sets are calculated and analysed. The highest accuracy of 92.14% is obtained based on the gradient features using 4200 (1400 Bangla +1400 Hindi + 1400 English) samples for training and 2100 (700 Bangla +700 Hindi +700 English) samples for testing.