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Master Thesis Master thesis To obtain a Master of Science Degree in Informatics and Communication Systems from the Merseburg University of Applied Sciences Subject: Tunisian truck license plate recognition using an Android Application based on Machine Learning as a detection tool Author: Supervisor: Achraf Boussaada Prof.Dr.-Ing. Rüdiger Klein Matr.-Nr.: 23542 Prof.Dr. Uwe Schröter Table of contents Chapter 1: Introduction ................................................................................................................................. 1 1.1 General Introduction: ................................................................................................................................... 1 1.2 Problem formulation: ................................................................................................................................... 1 1.3 Objective of Study: ........................................................................................................................................ 4 Chapter 2: Analysis ........................................................................................................................................ 4 2.1 Methodological approaches: ........................................................................................................................ 4 2.1.1 Actual approach: ................................................................................................................................... 4 2.1.2 Image Processing with OCR: ................................................................................................................. 6 2.1.3 Chosen approach: ................................................................................................................................. 7 Chapter 3: Artificial Intelligence & Machine Learning ..................................................................................... 8 3.1 Introduction: ................................................................................................................................................. 8 3.2 Types: ........................................................................................................................................................... 8 3.2.1 Supervised learning: ............................................................................................................................. 8 3.2.2 Unsupervised Learning: ........................................................................................................................ 9 3.2.3 Semi-supervised Learning: .................................................................................................................. 10 3.2.4 Reinforcement Learning: .................................................................................................................... 10 3.3 Techniques: ................................................................................................................................................. 12 3.3.1 SVM: ................................................................................................................................................... 12 3.3.2 Random Forest: .................................................................................................................................. 15 3.3.2.1 Decision Tree: ............................................................................................................................. 15 3.3.2.2 Random Forest: ........................................................................................................................... 17 3.3.2.3 Feature Importance: ................................................................................................................... 18 3.3.3 Deep Learning: .................................................................................................................................... 19 3.3.3.1 Deep Learning and Neural Networks: ......................................................................................... 20 3.3.3.2 Perceptron: ................................................................................................................................. 25 3.3.3.3 Feed Forward: ............................................................................................................................. 25 3.3.3.4 Recurrent Neural Network: ........................................................................................................ 25 3.3.3.5 Deep Convolutional Network: .................................................................................................... 26 3.3.4 Convolutional Neural Network: .......................................................................................................... 33 3.3.4.1 The Convolution operation: ........................................................................................................ 33 3.3.4.2 Motivation: ................................................................................................................................. 35 3.3.4.3 Pooling: ....................................................................................................................................... 40 3.3.4.4 Normalization: ............................................................................................................................ 41 3.3.4.5 Random or unsupervised Features: ............................................................................................ 42 3.3.4.6 Regularization: ............................................................................................................................ 42 3.3.4.7 Probability conversion: ............................................................................................................... 43 3.3.5 Transfer Learning: ............................................................................................................................... 46 3.3.5.1 Applications of Transfer Learning: .............................................................................................. 49 3.4 Recapitulation: ........................................................................................................................................... 51 Chapter 4: Implementation .......................................................................................................................... 52 4.1 Software & Tools: ....................................................................................................................................... 52 4.2 Object Detection API:.................................................................................................................................. 54 4.3 Model Training: .......................................................................................................................................... 54 4.4 Mobile implementation: ............................................................................................................................. 58 4.5 Optimization for mobile usage: .................................................................................................................. 62 4.5.1 Minimum Device Requirement: ......................................................................................................... 62 4.5.2 Removing training-only nodes: ........................................................................................................... 62 4.5.3 Recompiling TensorFlow inference library: ........................................................................................ 64 4.5.4 Retrain with mobile data: ................................................................................................................... 65 4.5.5 Reduce model loading time or memory footprint & improve RAM usage: ........................................ 66 4.5.6 Reduce model size: ............................................................................................................................. 67 4.5.7 Exploring Quantized Calculations: ...................................................................................................... 67 4.6 Recapitulation: ........................................................................................................................................... 68 Chapter 5: Summary .................................................................................................................................... 69 5.1 Discussion: .................................................................................................................................................. 69 5.1.1 Results: ............................................................................................................................................... 69 5.1.2 Future Improvements: ........................................................................................................................ 72 5.1.2.1 TensorFlow Lite: .......................................................................................................................... 72 5.1.2.2 Pre-trained models: .................................................................................................................... 74 5.1.2.3 Training and serving with Cloud TPUs: ....................................................................................... 74 5.2 Conclusion: ................................................................................................................................................
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