Identification of Non-Black Inks Using HSV Colour Space

Abstract

An important problem in questioned document examination is detection of alterations done by inserting words or additional lines of text. In this paper, we present a statistical pattern recognition driven approach that views it as a two- class problem. Given two sample words, one of which is a suspected alteration, it is necessary to determine if the two belong to the same class or different classes. Our approach is defined in two stages. We start with a 11-dimensional vector that comprises colour features defined in HSV space and texture features. During the training phase, we derive within-class and between-class LI distance distributions and identify an optimal threshold that minimizes Type I and Type II errors. During the second or test phase, we take a pair of unkown samples and use the threshold value obtained from the training phase to decide if the two belong to the same class or distinct classes. Our experimental results involving more than 95000 pairs of word images show that the approach gives an accuracy of over 90% for gel and roller pens and an accuracy of 85% for ball pen writings.