Offline handwritten signature recognition using polar-scale normalization

Abstract

Offline handwritten signature is still widely used for person verification in financial and business transactions. Most research in offline handwritten signature at-tempts to improve feature extraction and classification for the better recognition rate. The deformation and unsteadiness of handwritten signatures, such as direction, declination, and size, are also the key factors sensitive to recognition rate. Therefore, this paper focuses on the pre-processing phase, which is an alternative way to improve the accuracy and to make such factors stable. This study is based on the hypothesis; a table signature size is able to boost up the recognition rate. Therefore, polar-scale normalization (PSN) is proposed to scale signature size and make it stable. In this method, the signature images are transformed into the polar coordinate system consisting of polar distance and angle, and then normalized by ||norm||. The normalized distance is certainly estimated by polar coordinate that helps reduce the deformed images. The 5,739-sample signature images with 150 classes are used to test in the experiment. PSN provide the better performance, when compared with traditional normalization schemes including min-max, decimal, z-score and MAD normalizations. The results reveal that the proposed method can improve the average recognition rate up to 98.39%.