Ancient Geez Script Recognition Using Deep Convolutional Neural Network

FITEHALEW ANCIENT GEEZ SCRIPT RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORK DEMILEW ASHAGRIE ASHAGRIE A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF APPLIED SCIENCES OF NEAR EAST UNIVERSITY ANCIENT GEEZ SCRIPT RECOGNITION USINGANCIENT GEEZ DEEP CONVOLUTIONAL 2019 NETWORK DEEP NEURAL By FITEHALEW ASHAGRIE DEMILEW In Partial Fulfillment of the Requirements for the Degree of Master of Sciences in Software Engineering NEU NICOSIA, 2019 ANCIENT GEEZ SCRIPT RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORK A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF APPLIED SCIENCES OF NEAR EAST UNIVERSITY By FITEHALEW ASHAGRIE DEMILEW In Partial Fulfillment of the Requirements for the Degree of Master of Sciences in Software Engineering NICOSIA, 2019 Fitehalew Ashagrie DEMILEW: ANCIENT GEEZ SCRIPT RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORK Approval of Director of Graduate School of Applied Sciences Prof.Dr.Nadire CAVUS We certify this thesis is satisfactory for the award of the degree of Master of Sciences in Software Engineering Examine committee in charge: Assoc. Prof. Dr. Kamil DİMİLİLER Department of Automotive Engineering, NEU Assoc. Prof. Dr. Yöney KIRSAL EVER Department of Software Engineering, NEU Assist. Prof. Dr. Boran ŞEKEROĞLU Supervisor, Department of Information Systems Engineering, NEU I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original of this work. Name, Last name: Fitehalew Ashagrie Demilew Signature: Date: ACKNOWLEDGMENT My deepest gratitude is to my advisor, Assist. Prof. Dr. Boran ŞEKEROĞLU, for his encouragement, guidance, support, and enthusiasm with his knowledge. I am indeed grateful for his continuous support of finalizing this project and for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing this paper without him, everything would not be possible. Then, I would like to thank my mother Tirngo Assefaw, my father Ashagrie Demilew, my brother Baharu Ashagrie, and the rest of my family for their support, encouragement, and ideas. Without you, everything would have been impossible and difficult for me. ii Dedicated to my mother Tirngo Assefaw... ABSTRACT In this research paper, we have presented the design and development of an optical character recognition system for ancient handwritten Geez documents. Geez, alphabet contains 26 base characters and more than 150 derived characters which are an extension of the base alphabets. These derived characters are formed by adding different kinds of strokes on the base characters. However, this paper focusses on the 26 base characters and 3 of the punctuation marks of Geez alphabets. The proposed character recognition system compromises all of the necessary steps that are required for developing an efficient recognition system. The designed system includes processes like preprocessing, segmentation, feature extraction, and classification. The preprocessing stage includes steps such as grayscale conversion, noise reduction, binarization, and skew correction. Many languages require word segmentation however, the Geez language doesn’t require it so, the segmentation stage encompasses only line and character segmentation. Among the different character classification techniques, this paper presents a deep convolutional neural network approach. A deep CNN is used for feature extraction and character classification purposes. Furthermore, we have prepared a dataset containing a total of 22,913 characters in which 70% (16,038) was used for training, 20% (4,583) used for testing, and 10% (2,292) was used for validation purpose. A total of 208 pages were collected from EOTC and other places in order to prepare the dataset. For the case of proving the proposed model is effective and efficient also, we have designed a deep neural network architecture with 3 different hidden layers. Both of the designed models were trained with the same training dataset and their results show that the deep CNN model is better in every case. The deep neural model with 2 hidden layers achieves an accuracy of 98.128 with a model loss of 0.095 however, the proposed deep CNN model obtained an accuracy of 99.389% with a model loss of 0.044. Thus, the deep CNN architecture results with a better recognition accuracy for ancient Geez document recognition. Keywords: Ancient Document Recognition; Geez Document Recognition; Ethiopic Document Recognition; Deep Convolutional Neural Network iii ÖZET Bu araştırma makalesinde, eski el yazısı Geez belgeleri için bir optik karakter tanıma sisteminin tasarımını ve geliştirilmesini sunduk. Tanrım, alfabe 26 baz karakter ve baz alfabelerin bir uzantısı olan 150'den fazla türetilmiş karakter içeriyor. Bu türetilmiş karakterler, temel karakterlere farklı tür vuruşlar eklenerek oluşturulur. Bununla birlikte, bu makale 26 temel karaktere ve Geez alfabelerinin noktalama işaretlerinden 3'üne odaklanmaktadır. Önerilen karakter tanıma sistemi, verimli bir tanıma sistemi geliştirmek için gerekli olan tüm gerekli adımları yerine getirir. Tasarlanan sistem ön işleme, segmentasyon, özellik çıkarma ve sınıflandırma gibi işlemleri içerir. Ön işleme aşaması, gri tonlamalı dönüştürme, gürültü azaltma, ikilileştirme ve eğri düzeltme gibi adımları içerir. Birçok dil kelime bölümlendirmesini gerektirir, ancak Geez dili bunu gerektirmez, bölümleme aşaması sadece çizgi ve karakter bölümlendirmesini kapsar. Farklı karakter sınıflandırma teknikleri arasında, bu makale derin bir evrişimsel sinir ağı yaklaşımı sunmaktadır. Özellik çıkarma ve karakter sınıflandırma amacıyla derin bir CNN kullanılır. Ayrıca, eğitim için% 70 (16,038), test için% 20 (4,583) ve validasyon amacıyla% 10 (2,292) kullanılmış toplam 22,913 karakter içeren bir veri seti hazırladık. Veri setini hazırlamak için EOTC ve diğer yerlerden toplam 208 sayfa toplanmıştır. Önerilen modelin etkili ve verimli olduğunu kanıtlamak için, 3 farklı katmanı olan derin bir sinir ağı mimarisi tasarladık. Tasarlanan modellerin her ikisi de aynı eğitim veri seti ile eğitildi ve sonuçları, derin CNN modelinin her durumda daha iyi olduğunu gösteriyor. 2 gizli katmanı olan derin sinir modeli, 0.095 model kaybıyla 98.128 kesinliğe ulaşır, ancak önerilen derin CNN modeli 0.044 model kaybıyla% 99.389 hassasiyet elde etti. Böylece, derin CNN mimarisi, eski Geez belge tanıma için daha iyi bir tanıma doğruluğu ile sonuçlanır. Anahtar Kelimeler: Eski Belge Tanıma; Geez Belge Tanıma; Etiyopik Belge Tanıma; Derin Konvolüsyonlu Sinir Ağı iv TABLE OF CONTENTS ACKNOWLEDGMENT ........................................................................................................... ii ABSTRACT .............................................................................................................................. iii ÖZET ......................................................................................................................................... iv TABLE OF CONTENTS .......................................................................................................... v LIST OF TABLES .................................................................................................................... x LIST OF FIGURES ................................................................................................................. xi LIST OF ABBREVIATIONS ................................................................................................. xii CHAPTER 1 : INTRODUCTION 1.1. Background...................................................................................................................... 1 1.2. Overview of Artificial Neural Network .......................................................................... 3 1.2.1. Supervised learning .................................................................................................. 4 1.2.2. Unsupervised learning .............................................................................................. 4 1.2.3. Reinforcement learning ............................................................................................ 5 1.3. Statement of the Problem ................................................................................................ 5 1.4. The Significance of the Study ......................................................................................... 7 1.5. Objectives of the Study ................................................................................................... 8 1.5.1. General objective ...................................................................................................... 8 1.5.2. Specific objectives .................................................................................................... 8 1.6. Methodology.................................................................................................................... 8 1.6.1. Literature review ...................................................................................................... 9 1.6.2. Data acquisition techniques ...................................................................................... 9 1.6.3. Preprocessing methods ............................................................................................. 9 1.6.4. System modeling and implementation ....................................................................

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