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ON-LINE HANDWRITTEN ARABIC CHARACTER RECOGNITION BASED ON GENETIC ALGORITHM
تشخیص الاحرف العربیة بالكتابة المباشرة إعتمادا على الخوارزمیة الجینیة

Author: Haithem Abd Al-RaheemTaha هیثم عبد الرحیم طه
Journal: DIYALA JOURNAL OF ENGINEERING SCIENCES مجلة ديالى للعلوم الهندسية ISSN: 19998716/26166909 Year: 2012 Volume: 5 Issue: 1 Pages: 79-87
Publisher: Diyala University جامعة ديالى

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Abstract

ABSTRACT:- On-line Arabic handwritten character recognition is one of the most challenging problems in pattern recognition field. By now, printed Arabic character recognition and on-line Arabic handwritten recognition has been gradually practical, while offline Arabic handwritten character recognition is still considered as "The hardest problem to conquer" in this field due to its own complexity. Recently, it becomes a hot topic with the release of database, which is the first text-level database and is concerned about the area of realistic Arabic handwritten character recognition. At the realistic Arabic handwritten text recognition and explore two aspects of the problem. Firstly, a system based on segmentation-recognition integrated framework wasdeveloped for Arabic handwriting recognition. Secondly, the parameters of embedded classifier initialed at character-level training were discriminatively re-trained at string level. The segmentation-recognition integrated framework runs as follows: the written character is first over-segmented into primitive segments, and then the consecutive segments are combined into candidate patterns. The embedded classifier is used to classify all the candidate patterns in segmentation lattice. According to Genetic Algorithm (Crossover, mutation, and population), the system outputs the optimal path in segmentation-recognition lattice, which is the final recognition result. The embedded classifier is first trained at character level on isolated character and then the parameters are updated at string level on string samples.Keywords: Arabic Character, handwritten recognition, Genetic Algorithm.

الخلاصةان تشخیص كتابة الاحرف العربیة في الزمن الحقیقي یعتبر من اكثر التحدیات في مجال التعرف على الاشكال. وهناك دراسات حول التشخیص للاحرف العربیة ما زالت تعتبر من المشاكل المعقده في هذا المجال. الهدف من البحث هو التشخیص للاحرف العربیة بخط الید واستكشاف الجوانب الثلاثة في بحثنا، اولا ان النظامیقوم على ت شخیص بشكل مجزء ضمن الاطار المتكامل للتعرف على خط الید باللغة العربیة، ثانیا،ً ان البارامیترات الخاصة بالتصنیف لعملیة البدء عند مستوى التعلیم لحالة الحرف المخطوط یدویا تقوم بتمییز مستوى الحرف عند مستوىالتدریب.تشخیص المقاطع في اللوحة المهیئة لكتابة الحرف تكون حسب التالي: كتابة الحر ف المقاطع الاولیة، وبعد ذلك یتم جمع المقاطع على التوالي في نموذج ترشیحي.و ثم یتم تصنیف جمیع النماذج في شبكه عبار ة عن مقاطع. وبالاستناد الى الخوارزمیة الجینیة (التبادل، الطفرات، والتوزیع)، فان الاخراج یكون ناتج عن المسار الامثل في للنموذج الترشیحي وهي التي تعتبر الناتج النهائي. یتم تدریب المصنف الأول على مستوى جزءا لا یتجزأ وذلك بعزل حرف على حرف ومن ثم یتم تحدیث المعلمات على مستوى السلسلة على عینات تم حفظها في قاعدة بیانات لمقارنتها مستقبلا بالادخال الجدید.

Keywords

ABSTRACT:- On-line Arabic handwritten character recognition is one of the most challenging problems in pattern recognition field. By now --- printed Arabic character recognition and on-line Arabic handwritten recognition has been gradually practical --- while offline Arabic handwritten character recognition is still considered as "The hardest problem to conquer" in this field due to its own complexity. Recently --- it becomes a hot topic with the release of database --- which is the first text-level database and is concerned about the area of realistic Arabic handwritten character recognition. At the realistic Arabic handwritten text recognition and explore two aspects of the problem. Firstly --- a system based on segmentation-recognition integrated framework was developed for Arabic handwriting recognition. Secondly --- the parameters of embedded classifier initialed at character-level training were discriminatively re-trained at string level. The segmentation-recognition integrated framework runs as follows: the written character is first over-segmented into primitive segments --- and then the consecutive segments are combined into candidate patterns. The embedded classifier is used to classify all the candidate patterns in segmentation lattice. According to Genetic Algorithm --- Crossover --- mutation --- and population --- the system outputs the optimal path in segmentation-recognition lattice --- which is the final recognition result. The embedded classifier is first trained at character level on isolated character and then the parameters are updated at string level on string samples. Keywords: Arabic Character --- handwritten recognition --- Genetic Algorithm.

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