Offline Arabic handwritten isolated character recognition system using support vector machine and neural network
Other Title(s)
التعرف إلى الحروف العربية المنفصلة و المكتوبة بخط اليد باستخدام آلية دعم الموجه و الشبكة العصبية
Dissertant
al-Jabburi, Muhammad Anas Husayn
Thesis advisor
Comitee Members
Arabiyat, Abd al-Salam
Abu Hashim, Muhannad
University
Middle East University
Faculty
Faculty of Information Technology
Department
Computer Science Department
University Country
Jordan
Degree
Master
Degree Date
2017
English Abstract
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
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
86
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : The research methodology.
Chapter Four : Implementation and results.
Chapter Five : Conclusions and future work.
References.
American Psychological Association (APA)
al-Jabburi, Muhammad Anas Husayn. (2017). Offline Arabic handwritten isolated character recognition system using support vector machine and neural network. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-762684
Modern Language Association (MLA)
al-Jabburi, Muhammad Anas Husayn. Offline Arabic handwritten isolated character recognition system using support vector machine and neural network. (Master's theses Theses and Dissertations Master). Middle East University. (2017).
https://search.emarefa.net/detail/BIM-762684
American Medical Association (AMA)
al-Jabburi, Muhammad Anas Husayn. (2017). Offline Arabic handwritten isolated character recognition system using support vector machine and neural network. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-762684
Language
English
Data Type
Arab Theses
Record ID
BIM-762684
