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http://hdl.handle.net/20.500.11889/8363
Title: | An efficient, font independent word and character segmentation algorithm for printed Arabic text | Authors: | Qaroush, Aziz Jaber, Bassam Mohammad, Khader Washaha, Mahdi Maali, Eman Nayef, Nibal |
Keywords: | Arabic OCR;Machine learning;Neural networks (Computer science);Optical Character Recognition;Word segmentation;Speech perception - Arabic language;Character segmentation;Natural language processing (Computer science);Image analysis;Image processing - Digital techniques;Segmentation techniques;Baseline;Projection profile | Issue Date: | 2022 | Publisher: | Journal of King Saud University - Computer and Information Sciences | Abstract: | 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. | URI: | http://hdl.handle.net/20.500.11889/8363 | DOI: | 10.1016/j.jksuci.2019.08.013 |
Appears in Collections: | Fulltext Publications |
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An efficient, font independent word and character segmentation algorithm for printed Arabic text.pdf | 2.96 MB | Adobe PDF | View/Open |
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