Performance Study of Active Contour Model Based Character Segmentation with Nonlinear Diffusion

Abstract

In this paper, we present the combined character segmentation algorithm based on the active contour model and nonlinear diffusion techniques. The active contour model is used to perform segmentation of printed characters. The coherence enhancing diffusion technique is proposed to smooth out artifacts and background noises without destroying the edges. The performance of the two character segmentation methods: i) the combined ACM-FGM and CED algorithm, and ii) the ACM-FGM algorithm have been validated using a large scale printed documents in Hindi, Malayalam and Telugu text. The combined algorithm achieves an average segmentation accuracy of 89.08% whereas the ACM-FGM algorithm alone had an average accuracy of 52.63%. The whole character segmentation process time is lesser than that of the ACM-FGM algorithm alone. Experiments show that the combined algorithm provides promising results under scanned documents with different font-size and fond-style characters, and the different artifacts and background noises caused by the aging of the paper and diffusion.