Arabic text recognition
The issue of handwritten character recognition is still a big challenge to the scientific community. Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English pre-printed or handwritten characters space. Thus, the need to attempt a research re...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | article |
| منشور في: |
2004
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/5113 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php http://s3.amazonaws.com/academia.edu.documents/39259926/02-Ramzi.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1485853939&Signature=CuWNQKhbwYUX2QNIOKrOEhQ7vBs%3D&response-content-disposition=inline%3B%20filename%3DArabic_Text_Recognition.pdf |
| الوسوم: |
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| الملخص: | The issue of handwritten character recognition is still a big challenge to the scientific community. Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English pre-printed or handwritten characters space. Thus, the need to attempt a research related to Arabic handwritten text recognition. Algorithms based on neural networks have proved to give better results than conventional methods when applied to problems where the decision rules of the classification problem are not clearly defined. Two neural networks were built to classify already segmented characters of handwritten Arabic text. The two neural networks correctly recognized 73% of the characters. However, one hurdle was encountered in the above scenario, which can be summarized as follows: there are a lot of handwritten characters that can be segmented and classified into two or more different classes depending on whether they are looked at separately, or in a word, or even in a sentence. In other words, character classification, especially handwritten Arabic characters, depends largely on contextual information, not only on topographic features extracted from these characters. |
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