| 000 | 07178nam a22002537a 4500 | ||
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| 008 | 210112b2014 a|||f mb|| 00| 0 eng d | ||
| 040 |
_aEG-CaNU _cEG-CaNU |
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| 041 | 0 |
_aeng _beng |
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| 082 | _a610 | ||
| 100 | 0 |
_aIsmail Mohamed Sobhy _9196 |
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| 245 | 1 |
_aA 63-Way Character Recognition / _cIsmail Mohamed Sobhy |
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| 260 | _c2014 | ||
| 300 |
_a 96 p. _bill. _c21 cm. |
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| 500 | _3Supervisor: Mohamed A. El-Helw | ||
| 502 | _aThesis (M.A.)—Nile University, Egypt, 2014 . | ||
| 504 | _a"Includes bibliographical references" | ||
| 505 | 0 | _aContents: 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Advantages Of Acquiring Text . . . . . . . . . . . . . . . . 1 1.1.2 Types of Text . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.3 Text Extraction in Videos and Images . . . . . . . . . . . 2 1.2 Aim of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Background and Related Work 5 2.1 Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Natural Scene Text . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Text Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 The Traditional Text Extraction Pipeline . . . . . . . . . . . . . . 7 2.4.1 Text Detection and Localization . . . . . . . . . . . . . . . 8 2.4.1.1 Region Based Text Detection and Localization Technique: Stroke Width Transform [1] . . . . . 8 2.4.1.2 Texture Based Detection: A Laplacian Method for Video Text Detection [2] . . . . . . . . . . . . 12 v CONTENTS 2.4.2 Text Frame Selection/Classication . . . . . . . . . . . . . 13 2.4.2.1 Text Tracking: An Eective Video Text Tracking Algorithm Based on SIFT Feature and Geometric Constraint [3] . . . . . . . . . . . . . . . . . . . . 14 2.4.3 Text Enhancement . . . . . . . . . . . . . . . . . . . . . . 18 2.4.3.1 Text Enhancement: Edge based Binarization for Video Text Images[4] . . . . . . . . . . . . . . . . 19 2.4.4 Character Recognition . . . . . . . . . . . . . . . . . . . . 22 2.4.4.1 Tesseract . . . . . . . . . . . . . . . . . . . . . . 22 2.5 Alternative Methods . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.5.1 Character Detection . . . . . . . . . . . . . . . . . . . . . 24 2.5.2 Non-Maximal Suppression . . . . . . . . . . . . . . . . . . 25 2.5.3 Character Recognition . . . . . . . . . . . . . . . . . . . . 26 2.6 Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3 Algorithms Behind The Proposed Character and Background Recognition 29 3.1 Perface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Algorithms For Feature Extraction . . . . . . . . . . . . . . . . . 31 3.2.1 Histogram of Oriented Gradients (HOG) . . . . . . . . . . 31 3.2.2 Felzenszwalb's HOG[5] . . . . . . . . . . . . . . . . . . . . 34 3.3 Used Classier: Support Vector Machines(SVMs) . . . . . . . . . 37 3.3.1 Hard Margin . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.1.1 Primal Form . . . . . . . . . . . . . . . . . . . . 39 3.3.1.2 Dual Form . . . . . . . . . . . . . . . . . . . . . 39 3.3.2 Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.3.2.1 Linear Kernel . . . . . . . . . . . . . . . . . . . . 41 3.3.2.2 Radial Basis Function . . . . . . . . . . . . . . . 42 3.3.3 Soft Margin . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3.3.1 Updated Primal Form . . . . . . . . . . . . . . . 43 3.3.3.2 Updated Dual Form . . . . . . . . . . . . . . . . 44 3.4 Grid Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 vi CONTENTS 3.5 Data Synthesization: Synthetic Minority Over-sampling TEchnique (SMOTE)[6] . . . . . . . . . . . . . . . 46 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4 Results and Discussion 51 4.1 Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.1 Histogram of Oriented Gradients (HOG) and Felzenzwalb's HOG Conguration . . . . . . . . . . . . . . . . . . . . . . 51 4.2.2 Grid Search Conguration . . . . . . . . . . . . . . . . . . 52 4.2.3 Support Vector Machines (SVMs) Conguration . . . . . . 53 4.2.4 Available Datasets . . . . . . . . . . . . . . . . . . . . . . 53 4.2.5 Datasets' Requirements . . . . . . . . . . . . . . . . . . . 55 4.2.6 Datasets Combinations . . . . . . . . . . . . . . . . . . . . 57 4.2.6.1 First Combination . . . . . . . . . . . . . . . . . 57 4.2.6.2 Second Combination . . . . . . . . . . . . . . . . 58 4.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3.1 Confusion Matrix . . . . . . . . . . . . . . . . . . . . . . . 59 4.3.2 F-score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.4 Experiments & Results . . . . . . . . . . . . . . . . . . . . . . . . 63 4.4.1 63 Classes with HOG . . . . . . . . . . . . . . . . . . . . . 63 4.4.2 63 Classes with FHOG . . . . . . . . . . . . . . . . . . . . 65 4.4.3 62 Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.4.4 62 and 63 Classes vs [7] . . . . . . . . . . . . . . . . . . . 67 4.4.5 56 Classes with HOG . . . . . . . . . . . . . . . . . . . . . 68 5 Conclusion and Future work 71 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Bibliography | |
| 520 | 3 | _aAbstract: Text extraction from documents is an essential task which proved its contribution to many applications. The same benets can be gained when it comes for images and videos. However, text extraction from images and videos is not as advanced as in the case of documents. It is trailing behind because of the characteristics of natural scene text. One of the most important operations in any text extraction pipeline is the character recognition module. Character recognition is a substantial phase which needs to be performed in a better way so that it is improved and its dependence on other phases decrease. Its renement means better results for the whole text extraction process. With many advancements in object detection methods, it is an opportunity to introduce the same methodologies used for objects on text that appears in images and videos. The thesis aims to have a combined 63-way character recognition to deal with characters and background at same time. Moreover, the thesis deals with preparing datasets and synthesizing samples to train character recognition. The nal results will be compared versus normal 62-way character recognition which only works for characters. | |
| 546 | _aText in English, abstracts in English. | ||
| 650 | 4 |
_aInformatics-IFM _9266 |
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| 655 | 7 |
_2NULIB _aDissertation, Academic _9187 |
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| 690 |
_aInformatics-IFM _9266 |
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| 942 |
_2ddc _cTH |
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| 999 |
_c8809 _d8809 |
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