An efficient, font independent word and character segmentation algorithm for printed Arabic text
| Autor: | Khader Mohammad, Eman Maali, Aziz Qaroush, Nibal Nayef, Bassam Jaber, Mahdi Washaha |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
General Computer Science
Computer science Binary image Text segmentation 020206 networking & telecommunications Character segmentation Cursive script 02 engineering and technology QA75.5-76.95 Arabic OCR Segmentation techniques Character (mathematics) Word segmentation Baseline Electronic computers. Computer science Font 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Projection (set theory) Cursive Algorithm Word (computer architecture) |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 1, Pp 1330-1344 (2022) |
| ISSN: | 1319-1578 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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