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Research Article Open Access
Authorizing hand-written signature has always been a challenge to prevent illegal transactions, especially when the forged and the original signatures are very „similar-lookingâ in nature. In this paper, we aim to automate forged signature verification process, offline, using Adaptive Resonance Theory type-2 (ART-2), which has been implemented in „Câ language using both sequential and parallel programming. The said network has been trained with the original signature and tested with twelve very similar-looking but forged signatures. The mismatch threshold is set as 5%; however, it is set flexible as per the requirement from case-to-case. In order to obtain the desired result, the vigilance parameter (ρ) and the cluster size (m) has been tuned by carefully conducted parametric studies. The accuracy of the ART-2 net has been computed as almost 100% with ρ = 0.97 and m = 20.