An Agent-Based System for Printed/Handwritten Text Discrimination

The handwritten/printed text discrimination problem is a decision problem usually solved after a binarization of grey level or color images. The decision is usually made at the connected component level of a filtered image. These image components are labeled as printed or handwritten. Each component is represented as a point in a n dimensional space based on the use of n different features. In this paper we present the transformation of a (state of the art) traditional system dealing with the handwritten/printed text discrimination problem to an agent-based system. In this system we associate two different agents with the two different points of view (i.e. linearity and regularity) considered in the baseline system for discriminating a text, based on four (two for each agent) different features. We are also using argumentation for modeling the decision making mechanisms of the agents. We then present experimental results that compare the two systems by using images of the IAM handwriting database. These results empirically prove the significant improvement we can have by using the agent-based system.