Multi-Font Arabic Word Recognition Using Spectral Features
نویسندگان
چکیده
In this paper we present a new technique for recognising Arabic cursive words from scanned images of text. The approach is segmentation-free, and is applied to four different Arabic typefaces, where ligatures and overlaps pose challenges to segmentation-based methods. We transform each word into a normalised polar image, then we apply a two dimensional Fourier transform to the polar image. The resultant spectrum tolerates variations in size, rotation or displacement. Each word is represented by a template that includes a set of Fourier coeficients. The recognition is based on a normalised Euclidean distance from those templates.
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