تمييز الحروف العربية المعزولة المكتوبة بخط اليد باستخدام الشبكات العصبية == Recognition Of Isolated Handwritten Arabic Letters Using Neural Networks
Author name:
نهلة ظاهر حبيب بهية
Supervisor name:
منذر نعمان التكريتي
General topic:
Electrical, Electronic and Communications Engineering
Specific topic:
Electrical Engineering
Degree:
Master
University:
Mustansiriyah University - College Of Engineering - Department Of Electrical Engineering
Language:
English
University location:
Baghdad
First pages:
34T544 - p.pdf
Abstract:
يتناول هذا البحث مشكلة تمييز الحروف العربية المعزولة المكتوبة بخط اليد باستخدام الشبكات العصبية. ان نظام تمييز الحروف العربية المقترح يتكون من ثلاث مراحل وهي : مرحلة مسح صور الحروف العربية ثم مرحلة معالجة الصور واخيرا مرحلة التدريب والتصنيف.ان المرحلة الاخ | This thesis presents an algorithm for recognition of an off - line isolated handwritten Arabic letters using neural networks.The handwritten recognition system present in this work consists of three modules : - 1 - Arabic letter scanning module 2 - Arabic letter preprocessing module 3 - Learning and recognition module.The proposed neural network in the third module is trained in two stages : The first stage employs Self - Organizing Map learning algorithm for clustering the input pattern, which are based on a database of 196 letters collected from 7 independent persons.In the second stage each of the similar output cluster is considered as a subnet introduced to neural network trained by Back - propagation learning algorithm for classification. Several networks architecture are designed in the second stage using 1 - hidden and 2 - hidden layers with 5 and 28 output nodes in the output layer with different number of hidden nodes and learning rate.To examine the efficiency of the system a database of 196 letters collected from other 7 independent persons are used, (in order to test the ability of the trained network to generalize). The results show that clustering the input pattern, using 1 - hidden layer and 28 nodes in output layer improve the network performance. The system was implemented using (IBM - PC) of type Pentium 3.The programming language used to design the system was c++ version 5.02.
