Diacritic segmentation technique for arabic handwritten using region-based
Arabic is a broadly utilized alphabetic composition framework on the planet, and it has 28 essential letters. The letters in order was first used to compose messages in Arabic, most prominently the Qur'an the holy book of Islam. However, Arabic language has diacritics in the word or letters whi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute Of Advanced Engineering And Science (IAES)
2019
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| Online Access: | http://eprints.utem.edu.my/id/eprint/24344/2/014-%20AHMED%20ABDALLA.PDF http://eprints.utem.edu.my/id/eprint/24344/ http://ijeecs.iaescore.com/index.php/IJEECS/article/view/18676/13630 |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | Arabic is a broadly utilized alphabetic composition framework on the planet, and it has 28 essential letters. The letters in order was first used to compose messages in Arabic, most prominently the Qur'an the holy book of Islam. However, Arabic language has diacritics in the word or letters which are not something extra or discretionary to the language, rather they are a vital piece of it. By changing some diacritics may change both the syntax and semantics of a word by turning a word into another. However, the current researches address the foreground image and consider the diacritics as noises or secondary images. Thus, it is not suitable for Arabic handwritten. The diacritics will be removed from the image and this will lead to losing some good features. Furthermore, to extract the diacritics, the region-based segmentation technique is used. The image will be measured based on the region properties by first finding the connected component in binary image, and then we will determine the best area range measurement in that region for each image. The proposed technique region based has been tested in nine different images with different handwritten style, and successfully extracted
secondary foreground images (diacritics) for each image |
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