Learning-free, divide and conquer text-line extraction algorithm for printed Arabic text with diacritics
Aziz Qaroush, Abdalkarim Awad, Abualsoud Hanani, Khader Mohammad, Basam Jaber, Ala Hasheesh
Abstract
The extraction of text lines from document images is a critical step in optical character recognition. It is still considered an open document analysis problem. The presence of numerous font variations, diacritics, overlapping, and touching text-lines presents a challenge to algorithms designed for machine-printed text. In this paper, we present a simple and robust text-line extraction algorithm for printed Arabic text. The presented method is divided into two stages: preprocessing and text-line extraction. It extracts text-lines efficiently, even in small font sizes, by utilizing baselines, projection profiles, and a top-down divide and conquer technique. The presented method is fast and efficient when dealing with non-uniform inter-line spacing and the text-line overlapping problem. A set of experiments were conducted on the collected dataset. The experiments revealed that the proposed method outperforms two baseline approaches, with an average error rate of 3% on Arabic text without diacritics and 11% on Arabic text with diacritics. Furthermore, the experiments demonstrate that the proposed algorithm has a simple computational running time, with an average running time of 0.087 s per document image.