Short Words Signature Verification using Markov Chain and Fisher Linear Discriminant Approach

Writer identification is the domain of documents image analysis which popularly sound in many applications like banking, academic professional Optical Character Recognition (OCR) Signature verification remains one most important entities to authenticate document these applications. In view technical breakthrough, we have focused on short words signatures are very hard verify as they raise issues ambiguities. From geometrical studies signature-based images, it stated that morphology directional transformations (MDT) right extract suitable features case for writer identification. MDT takes data form Structure Element (SE). The morphological structures (DMS) SE used a key factor performing operations signature images. We adopted Markov chains and Fisher Linear Discriminant (FLD) computing gradients from line corresponding word. Neural network evaluate proposed model. training testing signature, images leave-one-out followed. Our purposed model tested NIST database extracting length with three letters. It observed simple architecture neural achieved 100% satisfactory results using words.