LICENSE PLATE RECOGNITION OF MOVING VEHICLES. Siti Rahimah Binti Abd Rahim

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1 LICENSE PLATE RECOGNITION OF MOVING VEHICLES Siti Rahimah Binti Abd Rahim Bachelor of Engineering with Honors (Electronics & Computer Engineering) 2009/2010

2 UNIVERSITI MALAYSIA SARAWAK R13a BORANG PENGESAHAN STATUS TESIS Judul: LICENSE PLATE RECOGNITION OF MOVING VEHICLES SESI PENGAJIAN: 2009/2010 Saya SITI RAHIMAH BINTI ABD RAHIM (HURUF BESAR) mengaku membenarkan tesis * ini disimpan di Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dengan syarat-syarat kegunaan seperti berikut: 1. Tesis adalah hakmilik Universiti Malaysia Sarawak. 2. Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dibenarkan membuat salinan untuk tujuan pengajian sahaja. 3. Membuat pendigitan untuk membangunkan Pangkalan Data Kandungan Tempatan. 4. Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi pengajian tinggi. 5. ** Sila tandakan ( ) di kotak yang berkenaan SULIT TERHAD (Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972). (Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/ badan di mana penyelidikan dijalankan). TIDAK TERHAD Disahkan oleh (TANDATANGAN PENULIS) (TANDATANGAN PENYELIA) Alamat tetap: NO. 1, JALAN 1, TAMAN BATU 30, BATANG KALI, DR. MOHD SAUFEE BIN MUHAMMAD Nama Penyelia SELANGOR DARUL EHSAN. Tarikh: Tarikh: CATATAN * Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah, Sarjana dan Sarjana Muda. ** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai SULIT dan TERHAD.

3 Final Year Project attached here: Title: License Plate Recognition of Moving Vehicles Author Name: Siti Rahimah Binti Abd Rahim Metric Number: Is hereby read and approved by: Dr. Mohd Saufee Bin Muhammad Date Project Supervisor

4 LICENSE PLATE RECOGNITION OF MOVING VEHICLES SITI RAHIMAH BINTI ABD RAHIM Thesis is submitted to Faculty of Engineering, University Malaysia Sarawak in Partial Fulfillment of the Requirements for Degree of Bachelor of Engineering with Honors (Electronics & Computer) 2009/2010

5 Dedicate this dissertation to my lovely parents, Abd Rahim Bin Mohd Ali and SitiSakniahBintiSarman and my siblings AbdRahiman Bin Abd Rahim and SitiRaihaniahBintiAbd Rahim for their love and being supportive family for me. ii

6 ACKNOWLEDGEMENTS My sincerest appreciation extended to my supervisor, Dr. Mohd Saufee bin Muhammad for guidance, support, advices, comments and suggestions throughout the process of completing this project. I would like to thank the Faculty of Engineering lecturers for the guidance given for pursuing engineering knowledge and skills during my years here. I express my thank to the Final Year Project coordinator, Madam Ade Syahida Wani for the information s and guidance s to complete the project report as the format that has been explained during Semester 1. A special thank goes to my family and friends for the supports, guidance s, comments, suggestions and encouragements to complete this project. Special thanks go those who are that upload their project on the internet or blog because these information s help a lot for the completion of the project. iii

7 ABSTRAK Pengecaman corak telah diketahui oleh ramai penganalisis sebagai satu aplikasi dalam rangkaian neural. Ramai penyelidik memilih tajuk ini sebagai bahan penyelidikan samada pengecaman huruf dan lain-lain corak. Pengecaman huruf adalah satu aplikasi yang paling terkenal dalam pengecaman corak samada huruf tulisan tangan atau lain-lain seperti huruf Arab and China. Projek ini melibatkan pengecaman huruf dari pendaftaran kenderaan sewaktu kenderaan sedang bergerak di atas jalan raya. Pendaftaran kenderaan mempunyai dua jenis huruf iaitu abjad dan nombor. Huruf dari pendaftaran kenderaan dapat dikecam dengan mengunakan teknik pemprocessan gambar dan aplikasi rangkaian neural yang terdapat dalam perisian MATLAB. Projek ini mempunyai dua bahagian, dimana gambar kenderaan akan diproses mengunakan teknik pemprosesan gambar dan kemudian akan dikecam mengunakan rangkaian neural. Pengecaman huruf dari pendaftaran kenderaan akan dikecam mengikut sasaran yang telah ditentukan. Seterusnya, perbandingan diantara 50 dan 100 neurons lapisan tersembunyi dilaksanakan untuk mengenalpasti pengecaman huruf yang terbaik. Pada peringkat akhir projek, pengenalpastian huruf akan dibentang dan dibincangkan. iv

8 ABSTRACT Pattern recognition has been identified by researchers as one of the neural network applications. There are many researches on this topic whether it character recognition or other pattern. The famous application in pattern recognition is the character recognition whether it handwritten recognition or others such as Arabic and Chinese character. In this project, the character recognition is for moving vehicles where character from license plate of moving vehicles will be recognized. License plate character consists of alphabet and number. Incorporated with image processing and neural network toolbox, this simulation will be design using the MATLAB toolbox. This project consists of two parts where the image will be process in image processing part while the character will be recognized using the backpropagation neural network. The character recognition will be recognizing according to the target output. In addition, performing recognition simulations compare between 50 and 100 neuron of hidden layer for the best character recognition. At the end of this project the recognized character from the license plate will be presented. v

9 TABLE OF CONTENTS Pages Dedication Acknowledgement Abstrak Abstract Table of Contents List of Tables List of Figures List of Abbreviations ii iii iv v vi x xi xiii Chapter 1 INTRODUCTION 1.1 Background 1.2 Project Objectives 1.3 Statement of Expected Problems 1.4 Proposed Solutions 1.5 Expected Outcomes 1.6 Report Outlines vi

10 Chapter 2 LITERATURE REVIEW 2.1 License Plate Recognition System 2.2 Image Preprocessing 2.3 Image Enhancement Contrast Manipulation Histogram Manipulation 2.4 Image Enhancement using Filters Sharpening Filter in Spatial Domain Sharpening Filter in Frequency Domain 2.5 Image Segmentation 2.6 Feature Extraction 2.7 Neural Network 2.8 Artificial Neural Network Feed-Forward Neural Network Recurrent Neural Network 2.9 Back-propagation Neural Network 2.10 Training Algorithm Supervised Training Unsupervised Training 2.11 Introduction to MATLAB What is MATLAB? The MATLAB System vii

11 Chapter 3 METHODOLOGY 3.1 Character Recognition Tools 3.2 Flow Chart of Character Recognition 3.3 Image Acquisition 3.4 Image Preprocessing Image Cropping Grayscale Conversion Image Enhancement 3.5 Neural Network in Character Recognition Network Creation Network Initialization Network Training Network Simulation Network Performance 3.6 Training Characters 3.7 Gradient Descent with Momentum BP (traingdm) 3.8 Training Object 3.9 Network Properties 3.10 Testing Set Images 3.11 Implementation of Graphical User Interface (GUI) Chapter 4 RESULTS AND DISCUSSIONS 4.1 Image Processing Process Step On Image Processing 4.2 GUI Results viii

12 4.3 Result Discussions 4.4 Number of Neuron in Hidden Layer 4.5 Number of Epochs 4.6 Learning Rate 4.7 Project Discussions Chapter 5 CONCLUSSIONS AND RECOMMENDATIONS 5.1 Project Achievements 5.2 Recommendation for Future Works REFERENCES 61 APPENDIX APPENDIX A APPENDIX B APPENDIX C APPENDIX D ix

13 LIST OF TABLES Tables Pages 3.1 Training Character with the Targets Network Properties Characters Result with Respective Compet Function 56 Answer x

14 LIST OF FIGURES Figures Pages 1.1 Standard Configuration of Malaysia License Plates Gray Level Transformation for Contrast 10 Enhancement 2.2 Gray Level Slicing Transformation Result Image of using Technique in Figure 2.2 (a) The Eight Bit Planes of the Eight Bit Image High and Bright Intensity Image with Histogram Low-contrast and High-contrast Image with 14 Histogram 2.7 Result Of Histogram Equalization And Their 15 Histogram 2.8 Result Images of Ideal Highpass Filter Extracting the Character from License Plate License Plate Character Feature Extracted using Kirsch Edge Detection Multilayer Net Architecture Feed-Forward Neural Network (Single Hidden 25 Layer) 2.14 Multilayer Feed-Forward Neural Network Basic Feedback Structure 26 xi

15 2.16 Back-propagation Net Flow Chart of Character Recognition Image Preprocessing Process Converting RGB to Grayscale Image Neural Network Design Neural Network Training Windows License Plate Recognition GUI Image of Moving Vehicles Load Image into GUI License Plate Image after Extraction Image of Seven License Plate Character Convert into Binary Image Load Image for Neural Network Test Recognized Character Uncompleted Recognize Characters License Plate with Segmentation Problem 57 xii

16 LIST OF ABBREVIATIONS ANN BP DSLR FYP MATLAB NN RGB UNIMAS GUI Artificial Neural Network Back-propagation Digital Single-lens Reflex Camera Final Year Project Matrix Laboratory Neural Network Red Green Blue University Malaysia Sarawak Graphical User Interface xiii

17 CHAPTER 1 INTRODUCTION 1.1 Background Vehicles license plate recognition is one of the important techniques that can be used for identification of vehicles around the world. It useful in many applications such as entrance admission, security, parking control, road traffic control, speed control and so on [1]. This project entitled License Plate Recognition of Moving Vehicles is a system that will be developed to recognize the license plate characters in various speeds and conditions of moving vehicles. This project consists of simulation program to recognize license plate characters where a captured image of moving vehicles will be the input. The image will then be processed and analyzed using image processing and neural network techniques. Based on network performance error calculated from neural network output and target output will determined whether the neural network recognize the input as the target. Figure 1.1 shows the standard layout configurations of license plates for Malaysian private vehicles [1]. 1

18 Figure 1.1: Standard Configuration of Malaysia License Plates 1.2 Project Objectives The project objectives of designing the software for license plate recognition as has been discussed with the supervisor have been identified, as follows:-. 1. Develop the coding to loading image and neural network training to recognizing the character from license plate of moving vehicles. 2. Develop the coding that can extract the character from single line pattern with seven character of license plate in Malaysia particularly Sarawak state. 3. Develop the coding for image processing process and neural network training. 2

19 1.3 Statement of Expected Problems The main expected problem that will be encountered in this project is blurred image that produce when capturing moving vehicles license plate image using normal camera. This problem occurs due to slow camera speed as compared to the moving vehicles. The blurred image capture also occurs when capturing image during the different conditions weather such as raining, sunshine, night and cloudy. Blurred image problem is basically causes by the hardware part of this project. The similarity of some alphabet and number patterns are also foreseen to the problems occur in this project. The alphabet and number that might be similar are misinterpreted by developed software are 1 with 7, 2 with Z and 8 with B. This may causes error where incorrect results are displayed by the simulation software developed 1.4 Proposed Solutions The solution on blurred image can be solved by using the appropriate camera that suitable for capture the moving image such as high speed camera. Although this camera speed can capture the freeze moving image, it is still incapable of solving the blurred image. The images will still certain same blurred edges. Therefore, the image must undergo some image processing technique using MATLAB software to remove the blur edges. After this process, the image obtain will be used to undergo the next part for character recognition. 3

20 The neural network approach will recognize each character that has been extract from image, where it will solve the misinterpreted character problem. The solution on recognizing the similar character will be also solved using neural network approach where the best chosen learning pattern identified will be used for this problem. There are a lot of neural network types can be chosen for better accuracy on recognizing the character. Large amount of neural network training in MATLAB software toolbox will give more understanding on this approach and increases the ability of this project to recognize the character correctly. 1.5 Expected Outcomes This FYP project is based on simulation program develop using MATLAB. However, this project still employs hardware system on the image acquisition part. This project consist the hardware used to capture the moving vehicles image and the simulation program used to do the image preprocessing and recognize the license plate character based on target output given in neural network training process. This simulation program will be developed using MATLAB software. Image preprocessing consist the process of image enhancement, filtering the noise and extracts the character from license plate where neural network part consists of process to recognize each license plate character based on error calculation between network output and target output. Video camera is used to capture the moving image and convert it into image frame. 4

21 1.6 Report Outlines The report outline contains the undergoing chapter of the final year project report. Chapter 1 starts with introduction of the project, benefits and also the aims and objective of the project. This chapter also gives explanation on the statement of problem together with the proposed solution on each problem and project outline that has been followed while undergoing the final year project. Chapter 2 is the literature review where it summarizes the recent research and scholarly sources relevant on the particular issue and theory in this project. This chapter also summarizes the particular of theory on simulation approach that connected with this project. In this context, the research on license plate recognition using another approach and the explanation on simulation approach such as image processing part will be discusses. Chapter 3 is methodology which summarize about the method that will be used in this project to obtain the result. In this project the method that will be used in recognizing the license plate character is image processing approach and neural network simulation using MATLAB. Chapter 4 explains the result and discussion from this project. The result represent in from of network performance graph, the network simulation result from the network training and the discussion on recognition result. 5

22 Chapter 5 concludes the report summary of the finding obtain though out the whole FYP project. The conclusion on work experience and work effort done to meet the requirement on this project development has been told here. The future work on improve this project and recommendation on new title research that similar with the project also been suggest here. 6

23 CHAPTER 2 LITERATURE REVIEW 2.1 License Plate Recognition System License Plate Recognition of Moving Vehicles is based on image processing and neural network where image processing techniques such as edge detection, thresholding and re-sampling has been used to locate and isolate the license plate and the characters. The neural network was used for successful recognition the license plate number [2]. There are many researches on this project title where they using a different method for license plate character recognition [1, 2, 3]. Among the thesis on license plate recognition system titles Vehicles license plate character recognition by neural network by M. Khalid et. al. [1], Smart License Plate Recognition System based on Image Processing using neural network by V. Koval et. al. [2] and car license plate recognition with neural network and fuzzy logic by J.A.G Nijhuis et. al. [3]. Some of the method that they used is almost same especially on the image preprocessing, image segmentation and also use the grayscale image. 7

24 2.2 Image Preprocessing Image preprocessing is an important process which it used to manipulate the images for character recognition operation. The image preprocessing applies some standard image processing technique such as contrast stretching and noise filtering to enhance the quality of the image [3]. Capturing image of moving object will produce the blurred image, using the computer algorithm; image will be preprocessing to improve the quality to allow the character in the images to be recognized. In image preprocessing, color image (RGB) acquired by a digital camera is converted to grayscale image based on the RGB to gray-scale conversion technique. The basic idea of this conversion is performed by eliminating the hue and saturation information while retaining the luminance. Equation (2.1) shows an optimal method for RGB to grayscale conversion [4]. (2.1) where Lu is luminance R refers to red components G refers to green components B refers to blue components 8

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