Segmentation of Printed Farsi/Arabic Words

Characters connectivity is a problem in automated printed Farsi/Arabic script recognition. This paper introduces a novel scheme based on wavelet transform to solve segmentation of printed Farsi/Arabic words into characters. Our novel algorithm employs a new wavelet transform by which the extracted wavelet coefficients are exploited, in detecting, underlying horizontal edges and base line. Projection of horizontal edges and their location on base line provide the segmentation points. A classification method distinguishes true segmenting points. New algorithm is robust against noise, gray level, font and size of characters. Simulation results provide a comparison between new algorithm and three schemes, closed contour, structural and holistic, in terms of precision, speed and robustness against Gaussian noise. Experimental Results indicate superiority of our scheme in terms of precision and show that new algorithm improves recognition speed by a factor of at least 2.5 times.