Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving

Abstract

Inspired from human perception and common text documents characteristics based on readability constraints, an Arabic text line segmentation approach is proposed using seam carving. Taking the gray scale of the image as input data, this technique offers better results at extracting handwritten text lines without the need for the binary representation of the document image. In addition to its fast processing time, its versatility permits to process a multitude of document types, especially documents presenting low text-to-background contrast such as degraded historical manuscripts or complex writing styles like cursive handwriting. Even if our focus in this paper was on Arabic text segmentation, this method is language independent. Tests on a public database of 123 handwritten Arabic documents showed a line detection rate of 97.5% for a matching score of 90%.

Editor’s Note

“…Going back to recognition tasks but, this time, of handwritten characters, M. Daldali and A. Souhar [11] propose an Arabic text line segmentation approach using seam carving. The technique offers satisfactory results at extracting handwritten text lines without the need for the binary representation of the document image, even with documents presenting low text-to-background contrast such as degraded historical manuscripts.…”