Writer Identification Using Steered Hermite Features and SVM

Abstract

Writer recognition is considered as a difficult problem to solve due to variations found in the writing, even from the same writer. In this paper, steered Hermite features are used to identify writer from a written document. We will show that steered Hermite features are highly useful for text images because they extract lot of information, notably for data characterized by oriented features, curves and segments. The algorithm we propose here, first calculates the steered Hermite features of the images which are then passed on to support vector machine for training and testing. The base of tests consists of sample of some lines of writings (five at most) of primarily diversified writings of authors from IAM database. With the proposed algorithm based on steered Hermite features, we were able to achieve an accuracy of around 83% percent for a set of 30 authors with non overlapping images of written text.