Arabic writer identification based on power spectrum and laws' filters methods
مقدم أطروحة جامعية
مشرف أطروحة جامعية
أعضاء اللجنة
Rashid, Abd Allah Abd al-Ali
Haddad, Bassam
Salamah, Walid Khalid
Hammu, Bassam
الجامعة
الأكاديمية العربية للعلوم المالية و المصرفية
الكلية
كلية نظم و تكنولوجيا المعلومات
القسم الأكاديمي
قسم نظم المعلومات الحاسوبية
دولة الجامعة
الأردن
الدرجة العلمية
دكتوراه
تاريخ الدرجة العلمية
2006
الملخص الإنجليزي
Many methods have been reported for handwriting-based writer identification.
None of these methods assumes that this technique is possible in Arabic.
In this dissertation, we present two new methods for feature extraction of handwriting texture.
These two methods are based on signal processing techniques : the first is based on Power Spectrum (PS) ; whereas the second is based on Laws' Filtering (LF).
Their effectiveness compared to multiple channels (Gabor) filters and the gray-level co-occurrence matrix (GLCM) is shown.
The Gabor and the GLCM are well known methods proved to yield high performance in writer identification in Latin.
In our experimentations, texture features are extracted for wide range of frequency and orientations.
This is mainly due to the nature of Arabic handwritten which constitutes more spreaders compared to Latin.
The most discriminate features are selected using a proposed model for feature selection based on hybrid Support Vectors Machine (SVM) / and Genetic Algorithm (GA) techniques.
Three classification methods are used for classification, namely; Linear Discriminate Classifier (LDC), Support Vectors Machine (SVM), and the K nearest-neighbors (K-NN) classifier.
Experiments are made using Arabic handwritings from 20 different people and very promising results of 90 % identification rate is achieved.
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
عدد الصفحات
97
قائمة المحتويات
Table of contents.
Abstract.
Chapter One : introduction.
Chapter Two : normalization.
Chapter Three : feature extraction using texture analysis.
Chapter Four : feature ranking and selection.
Chapter Five : classification.
Chapter Six : experiments and results.
Chapter Seven : conclusions and future work.
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Dumur, Ayman. (2006). Arabic writer identification based on power spectrum and laws' filters methods. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-306829
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Dumur, Ayman. Arabic writer identification based on power spectrum and laws' filters methods. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences. (2006).
https://search.emarefa.net/detail/BIM-306829
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Dumur, Ayman. (2006). Arabic writer identification based on power spectrum and laws' filters methods. (Doctoral dissertations Theses and Dissertations Master). Arab Academy for Financial and Banking Sciences, Jordan
https://search.emarefa.net/detail/BIM-306829
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-306829
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
منصة "معرفة" للكتاب العربي الجامعي الرقمي 
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
