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Skripsie.out
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Skripsie.out
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\BOOKMARK [0][]{Doc-Start}{Abstract}{}% 1
\BOOKMARK [0][]{Doc-Start}{Uittreksel}{}% 2
\BOOKMARK [0][]{chapter*.2}{List of Figures}{}% 3
\BOOKMARK [0][]{chapter*.3}{List of Tables}{}% 4
\BOOKMARK [0][]{chapter*.4}{Nomenclature}{}% 5
\BOOKMARK [0][]{chapter.1}{1 Introduction}{}% 6
\BOOKMARK [1][]{section.1.1}{1.1 Problem background}{chapter.1}% 7
\BOOKMARK [1][]{section.1.2}{1.2 Problem statement}{chapter.1}% 8
\BOOKMARK [1][]{section.1.3}{1.3 Project scope and assumptions}{chapter.1}% 9
\BOOKMARK [1][]{section.1.4}{1.4 Project objectives}{chapter.1}% 10
\BOOKMARK [1][]{section.1.5}{1.5 Research methodology}{chapter.1}% 11
\BOOKMARK [1][]{section.1.6}{1.6 Graphical overview of system}{chapter.1}% 12
\BOOKMARK [0][]{chapter.2}{2 Literature study}{}% 13
\BOOKMARK [1][]{section.2.1}{2.1 Existing Optical Marker Recognition techniques}{chapter.2}% 14
\BOOKMARK [2][]{subsection.2.1.1}{2.1.1 Standard Optical Marker Recognition systems}{section.2.1}% 15
\BOOKMARK [3][]{subsubsection.2.1.1.1}{2.1.1.1 Finding the template}{subsection.2.1.1}% 16
\BOOKMARK [3][]{subsubsection.2.1.1.2}{2.1.1.2 Processing a bubble}{subsection.2.1.1}% 17
\BOOKMARK [1][]{section.2.2}{2.2 Optical character recognition}{chapter.2}% 18
\BOOKMARK [2][]{subsection.2.2.1}{2.2.1 Probabilistic approach}{section.2.2}% 19
\BOOKMARK [1][]{section.2.3}{2.3 Conclusion: System requirements}{chapter.2}% 20
\BOOKMARK [0][]{chapter.3}{3 Image processing}{}% 21
\BOOKMARK [1][]{section.3.1}{3.1 Orientation detection}{chapter.3}% 22
\BOOKMARK [2][]{subsection.3.1.1}{3.1.1 Initial filtering and orientation detection}{section.3.1}% 23
\BOOKMARK [2][]{subsection.3.1.2}{3.1.2 Radon transform}{section.3.1}% 24
\BOOKMARK [2][]{subsection.3.1.3}{3.1.3 Finding the template}{section.3.1}% 25
\BOOKMARK [1][]{section.3.2}{3.2 Bubble detection and processing}{chapter.3}% 26
\BOOKMARK [1][]{section.3.3}{3.3 Data processing and grading}{chapter.3}% 27
\BOOKMARK [1][]{section.3.4}{3.4 Conclusion}{chapter.3}% 28
\BOOKMARK [0][]{chapter.4}{4 Machine learning approach}{}% 29
\BOOKMARK [1][]{section.4.1}{4.1 Character recognition using a neural network}{chapter.4}% 30
\BOOKMARK [2][]{subsection.4.1.1}{4.1.1 Preprocessing and creating digit images}{section.4.1}% 31
\BOOKMARK [2][]{subsection.4.1.2}{4.1.2 Classification of digits}{section.4.1}% 32
\BOOKMARK [3][]{subsubsection.4.1.2.1}{4.1.2.1 The Neural Network basics}{subsection.4.1.2}% 33
\BOOKMARK [3][]{subsubsection.4.1.2.2}{4.1.2.2 The artificial neuron}{subsection.4.1.2}% 34
\BOOKMARK [3][]{subsubsection.4.1.2.3}{4.1.2.3 Generating an output from the network}{subsection.4.1.2}% 35
\BOOKMARK [3][]{subsubsection.4.1.2.4}{4.1.2.4 Deep Convolutional Neural Network}{subsection.4.1.2}% 36
\BOOKMARK [3][]{subsubsection.4.1.2.5}{4.1.2.5 Training of the neural network}{subsection.4.1.2}% 37
\BOOKMARK [1][]{section.4.2}{4.2 Probabilistic Graphical Models}{chapter.4}% 38
\BOOKMARK [2][]{subsection.4.2.1}{4.2.1 Overview of the system}{section.4.2}% 39
\BOOKMARK [2][]{subsection.4.2.2}{4.2.2 Estimating the intended digit}{section.4.2}% 40
\BOOKMARK [2][]{subsection.4.2.3}{4.2.3 Estimating the student answer}{section.4.2}% 41
\BOOKMARK [2][]{subsection.4.2.4}{4.2.4 Estimating the student number}{section.4.2}% 42
\BOOKMARK [2][]{subsection.4.2.5}{4.2.5 Training of a Probabilistic Graphical Model}{section.4.2}% 43
\BOOKMARK [1][]{section.4.3}{4.3 Conclusion}{chapter.4}% 44
\BOOKMARK [0][]{chapter.5}{5 Analysis of results}{}% 45
\BOOKMARK [1][]{section.5.1}{5.1 Results of 25 test cases}{chapter.5}% 46
\BOOKMARK [2][]{subsection.5.1.1}{5.1.1 Basic system}{section.5.1}% 47
\BOOKMARK [3][]{subsubsection.5.1.1.1}{5.1.1.1 Clash list}{subsection.5.1.1}% 48
\BOOKMARK [3][]{subsubsection.5.1.1.2}{5.1.1.2 Incorrect automatic graded results}{subsection.5.1.1}% 49
\BOOKMARK [2][]{subsection.5.1.2}{5.1.2 Complete system}{section.5.1}% 50
\BOOKMARK [3][]{subsubsection.5.1.2.1}{5.1.2.1 Clash list}{subsection.5.1.2}% 51
\BOOKMARK [3][]{subsubsection.5.1.2.2}{5.1.2.2 Incorrect automatic graded results}{subsection.5.1.2}% 52
\BOOKMARK [2][]{subsection.5.1.3}{5.1.3 Analysis of results}{section.5.1}% 53
\BOOKMARK [1][]{section.5.2}{5.2 Grading of tutorial tests}{chapter.5}% 54
\BOOKMARK [2][]{subsection.5.2.1}{5.2.1 Marking statistics}{section.5.2}% 55
\BOOKMARK [2][]{subsection.5.2.2}{5.2.2 Clash list}{section.5.2}% 56
\BOOKMARK [2][]{subsection.5.2.3}{5.2.3 Incorrect automatic graded results}{section.5.2}% 57
\BOOKMARK [2][]{subsection.5.2.4}{5.2.4 Conclusion}{section.5.2}% 58
\BOOKMARK [0][]{chapter.6}{6 Summary and conclusions}{}% 59
\BOOKMARK [1][]{section.6.1}{6.1 Project summary}{chapter.6}% 60
\BOOKMARK [1][]{section.6.2}{6.2 How this final year project benefits society}{chapter.6}% 61
\BOOKMARK [1][]{section.6.3}{6.3 What the student learned}{chapter.6}% 62
\BOOKMARK [1][]{section.6.4}{6.4 Future improvements}{chapter.6}% 63
\BOOKMARK [1][]{section.6.5}{6.5 Summary and conclusions}{chapter.6}% 64
\BOOKMARK [0][]{chapter*.31}{References}{}% 65
\BOOKMARK [0][]{appendix.A}{A Project plan}{}% 66
\BOOKMARK [0][]{appendix.B}{B Outcome compliance}{}% 67
\BOOKMARK [0][]{appendix.C}{C Mathematical and graphical description of system}{}% 68
\BOOKMARK [1][]{section.C.1}{C.1 High-level overview}{appendix.C}% 69
\BOOKMARK [1][]{section.C.2}{C.2 The student answer}{appendix.C}% 70
\BOOKMARK [1][]{section.C.3}{C.3 The intended digit}{appendix.C}% 71
\BOOKMARK [1][]{section.C.4}{C.4 The student number}{appendix.C}% 72
\BOOKMARK [0][]{appendix.D}{D Systems diagrams and software}{}% 73
\BOOKMARK [1][]{section.D.1}{D.1 Software}{appendix.D}% 74
\BOOKMARK [1][]{section.D.2}{D.2 Interface}{appendix.D}% 75
\BOOKMARK [1][]{section.D.3}{D.3 Templates}{appendix.D}% 76
\BOOKMARK [0][]{appendix.E}{E Validation and results}{}% 77
\BOOKMARK [1][]{section.E.1}{E.1 All tutorial results}{appendix.E}% 78
\BOOKMARK [2][]{subsection.E.1.1}{E.1.1 Overview}{section.E.1}% 79
\BOOKMARK [1][]{section.E.2}{E.2 Deep Convolutional Neural Network results}{appendix.E}% 80
\BOOKMARK [2][]{subsection.E.2.1}{E.2.1 Trained on generated database}{section.E.2}% 81
\BOOKMARK [3][]{subsubsection.E.2.1.1}{E.2.1.1 Accuracy of network}{subsection.E.2.1}% 82
\BOOKMARK [3][]{subsubsection.E.2.1.2}{E.2.1.2 Conclusion on accuracy}{subsection.E.2.1}% 83
\BOOKMARK [2][]{subsection.E.2.2}{E.2.2 Trained on MNIST database}{section.E.2}% 84
\BOOKMARK [3][]{subsubsection.E.2.2.1}{E.2.2.1 Accuracy of network}{subsection.E.2.2}% 85
\BOOKMARK [3][]{subsubsection.E.2.2.2}{E.2.2.2 Conclusion on accuracy}{subsection.E.2.2}% 86
\BOOKMARK [2][]{subsection.E.2.3}{E.2.3 Trained on mixed database}{section.E.2}% 87
\BOOKMARK [3][]{subsubsection.E.2.3.1}{E.2.3.1 Accuracy of network}{subsection.E.2.3}% 88
\BOOKMARK [3][]{subsubsection.E.2.3.2}{E.2.3.2 Conclusion on accuracy}{subsection.E.2.3}% 89