An efficient, font independent word and character segmentation algorithm for printed Arabic text

Journal of King Saud University: Computer and Information Sciences

Bassam Jaber¹  Mahdi Washaha²  Aziz Qaroush²  Khader Mohammad²  Eman Maali²  Nibal Nayef² 

[1] Corresponding author.;Department of Electrical and Computer Engineering, Birzeit University, Palestine;

关键词: Arabic OCR;    Word segmentation;    Character segmentation;    Cursive script;    Segmentation techniques;    Baseline;   

DOI  :  

来源: DOAJ

【 摘 要 】

Characters segmentation is a necessity and the most critical stage in Arabic OCR system. It has attracted the interest of a wide range of researchers. However, the nature of the Arabic cursive script poses extra challenges that need further investigation. Therefore, having a reliable and efficient Arabic OCR system that is independent of font variations is highly required. In this paper, an indirect, font-in dependent word and character segmentation algorithm for printed Arabic text investigated. The proposed algorithm takes a binary line image as an input and produces a set of binary images consisting of one character or ligature as an output. The segmentation performed at two levels: a word segmentation performed in the first level, by employing a vertical projection at the input line image along with using Interquartile Range (IQR) method to differentiate between word gaps and within word gaps. A projection profile method used as a second level of segmentation along with a set of statistical and topological features, which are font-independent, to identify the correct segmentation points from all potential points. The APTI dataset used to test the proposed algorithm with a variety of font type, size, and style. The algorithm experimented on 1800 lines (approximately 24,816 words) with an average accuracy of 97.7% for words segmentation and 97.51% for characters segmentation.

【 授权许可】

Unknown