Offline Arabic handwritten isolated character recognition system using support vector machine and neural network

العناوين الأخرى

التعرف إلى الحروف العربية المنفصلة و المكتوبة بخط اليد باستخدام آلية دعم الموجه و الشبكة العصبية

مقدم أطروحة جامعية

al-Jabburi, Muhammad Anas Husayn

مشرف أطروحة جامعية

Abu Saymah, Hisham

أعضاء اللجنة

Arabiyat, Abd al-Salam
Abu Hashim, Muhannad

الجامعة

جامعة الشرق الأوسط

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم علم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2017

الملخص الإنجليزي

Nowadays and because of the high expanses in technologies, a need to recognize a handwritten characters, words, and even sentences is being popped up.

Especially for education and business institutions.

Optical Character Recognition (OCR) programs eliminate human error, which can occur while the data is being input.

The Arabic Language had a little attention in this field compared with other languages due to the high cursive nature of the handwritten Arabic language, especially with their dots.

The difficulty lies in the complexity of locating the wavy shape in the characters, which solved by the combination of certain features extraction methods that work in separate way.

In this thesis, the proposed of Isolated Arabic off-line handwritten recognition system based on two stages classifiers (Hybrid).

First stage is a linear Support Vector Machine (SVM) for splitting the dataset characters into two groups - Characters with dots and Characters without dots, by giving certain extraction features to each group.

This division can reduce the error rate of characters recognition which has similar looking shape.

Second stage supplies the first stage result to Neural Network (NN) stage which granted one of the best correctness and accuracy by training.

Finally, a fully recognized character is acquired successfully.

This work is implemented using Institut of Communications Technology/ Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT) dataset, the system significantly reduce the load of NN process by SVM classifier, which can be used for real-time applications.

A total accuracy of this proposed work reaches 92.2% and in future work we look forward to getting higher rank of accuracy

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• التعرف على الحروف
• الشبكات العصبية(الحاسبات الإلكترونية)

عدد الصفحات

86

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-762684