Non-Redundant Contour Directional Feature Vectors for Character Recognition

Abstract

This paper presents a novel shape based feature for printed character recognition. The shape features are derived from the contour of the character which is unique to all characters. Preprocessing is performed to standardize the characters and handle all variations such as bold, italics and bold-italics font characteristics. The complete character set is clustered into different groups based on contour feature. A probe character is mapped into the corresponding cluster prior to recognition. This helps to reduce the computational overhead. Finally two recognition schemes have been proposed, based on angle feature extracted from the contour information and a longest common substring (LCS) based feature. Simulation has been carried out to validate the efficacy of the proposed scheme on printed Odia characters. Performance accuracy has been compared with the existing schemes. In general, it is observed that the proposed scheme outperforms the competitive schemes.