Abstract
Signature is widely used and developed area of research for personal verification and authentication. In this paper, we present a new offline handwritten signature recognition system based on fusion of global and GLCM (Grey Level Co-occurrence Matrix) features using fuzzy logic system as classifier tool. The global and GLCM features are fused to generate vector of 15 features for the verification of the signature. The test signature is compared with the database signatures based on features, whilst match/non match of signatures is decided with fuzzy logic. The experimental results obtained by using a database of 7 individuals’ signatures. A total number of 70 images are collected for our study and with average 10 signatures for each person, 5 of the signatures are used as training, the remaining 5signatures are used as testing group. The results show that the proposed modular architecture can achieve 100% recognition accuracy for training group and 90.5% recognition accuracy for the testing group with running time is 1.17 second.
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