Handwritten Signature Verification using Haar Cascade Classifier Approach

Abstract

Handwritten Signature verification is an important personal identification. It is widely used in authorizing a cheque or legal documents. Signature verification is either online (dynamic) or offline (static). A machine learning, Haar Cascade Classifier (HCC) approach was introduced by Viola and Jones [1] to achieve rapid object detection based on a boosted cascade of Haar-like features. Here, for the first time the HCC approach was applied for the handwritten signature recognition and verification. Two datasets were used, UTSig dataset [2] for Persian writers which written from right to left and includes 8,280 images from 115 writers. GPDS synthetic Signature database [3] for English writers which written from left to right. It contains data from 4,000 synthetic individuals. A classifier was created for each one of the writers’ signatures after applying the preprocessing phase. Each classifier was trained and tested using enormous number of signatures generated from applying artificial noises on the signature images. The system was tested with real signature images and produced accuracy of 92.42% with UTSig dataset and 92% with GPDS dataset. The proposed approach achieved high results compared to approaches built using traditional machine learning approaches. The approach proof applicability with the different types of writers’ languages and style.