Automatic License Plate Recognition
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1 Nijad Ashraf, Sajjad KM, Shehzad Abdulla, Saalim Jabir Dept. of CSE, MESCE Guide: Sajith N July 13, 2010
2 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
3 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
4 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
5 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
6 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
7 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
8 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
9 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
10 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
11 (ALPR) is a real time embedded mass surveillance system that captures the image of vehicles and recognizes their license number. ALPR technology tends to be region-specific, owing to plate variation from place to place. The crude system was invented in 1976 at the Police Scientific Development Branch in the UK. And none for India.
12 (ALPR) is a real time embedded mass surveillance system that captures the image of vehicles and recognizes their license number. ALPR technology tends to be region-specific, owing to plate variation from place to place. The crude system was invented in 1976 at the Police Scientific Development Branch in the UK. And none for India.
13 (ALPR) is a real time embedded mass surveillance system that captures the image of vehicles and recognizes their license number. ALPR technology tends to be region-specific, owing to plate variation from place to place. The crude system was invented in 1976 at the Police Scientific Development Branch in the UK. And none for India.
14 The first arrest due to a detected stolen car was made in Uses series of image manipulation techniques to detect, normalise and enhance the image of the number plate. Optical Character Recognition (OCR) to extract the alphanumerics of the licence plate. Active research area for implementing fool proof solution with international support.
15 The first arrest due to a detected stolen car was made in Uses series of image manipulation techniques to detect, normalise and enhance the image of the number plate. Optical Character Recognition (OCR) to extract the alphanumerics of the licence plate. Active research area for implementing fool proof solution with international support.
16 The first arrest due to a detected stolen car was made in Uses series of image manipulation techniques to detect, normalise and enhance the image of the number plate. Optical Character Recognition (OCR) to extract the alphanumerics of the licence plate. Active research area for implementing fool proof solution with international support.
17 Making the process fast, effective and cost efficient Some applications of the system are: Automated traffic surveillance and tracking system. Automated high-way/parking toll collection systems. Automation of petrol stations. Journey time monitoring.
18 Making the process fast, effective and cost efficient Some applications of the system are: Automated traffic surveillance and tracking system. Automated high-way/parking toll collection systems. Automation of petrol stations. Journey time monitoring.
19 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
20 ALPR systems have been implemented in many countries like Australia, Korea and few others. These countries enforced standards on the license plates in terms of dimensions, borders, colour, font size and type. Thus making the system easy to implement.
21 ALPR systems have been implemented in many countries like Australia, Korea and few others. These countries enforced standards on the license plates in terms of dimensions, borders, colour, font size and type. Thus making the system easy to implement.
22 Indian situation is very different, as we cannot expect the strict following of the standards,making the system complicated. Wide variations are found in font size, type, shape and colours. Hitachi offers a near real solution right now for a cost of more than Rs. 1000K per single license. s have been implemented using proprietary tools and libraries.
23 Indian situation is very different, as we cannot expect the strict following of the standards,making the system complicated. Wide variations are found in font size, type, shape and colours. Hitachi offers a near real solution right now for a cost of more than Rs. 1000K per single license. s have been implemented using proprietary tools and libraries.
24 Indian situation is very different, as we cannot expect the strict following of the standards,making the system complicated. Wide variations are found in font size, type, shape and colours. Hitachi offers a near real solution right now for a cost of more than Rs. 1000K per single license. s have been implemented using proprietary tools and libraries.
25 Indian situation is very different, as we cannot expect the strict following of the standards,making the system complicated. Wide variations are found in font size, type, shape and colours. Hitachi offers a near real solution right now for a cost of more than Rs. 1000K per single license. s have been implemented using proprietary tools and libraries.
26 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
27 Understand the image processing techniques involved. Realize the issues and challenges for implementing the system. Gain basic project management skills. Familiarize several tools for developing an intuitive system. Develop basic document writing and presentation skills.
28 Understand the image processing techniques involved. Realize the issues and challenges for implementing the system. Gain basic project management skills. Familiarize several tools for developing an intuitive system. Develop basic document writing and presentation skills.
29 Understand the image processing techniques involved. Realize the issues and challenges for implementing the system. Gain basic project management skills. Familiarize several tools for developing an intuitive system. Develop basic document writing and presentation skills.
30 Understand the image processing techniques involved. Realize the issues and challenges for implementing the system. Gain basic project management skills. Familiarize several tools for developing an intuitive system. Develop basic document writing and presentation skills.
31 Understand the image processing techniques involved. Realize the issues and challenges for implementing the system. Gain basic project management skills. Familiarize several tools for developing an intuitive system. Develop basic document writing and presentation skills.
32 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
33 The proposed system consist of 6 phases Design Licensing
34 Data Flow Diagram
35 Capturing Image Design Licensing The image of the vehicle is captured using a high resolution photographic camera. To understand the variations in settings like exposure, frame aperture etc, we have choosen 3 cameras. Canon 1000D High resolution DSLR camera. HD images. Canon PowerShot IS MP digital camera with Image Stabilization. Nokia E72 5 MP digital camera embedded on a mobile phone.
36 Capturing Image Design Licensing The image of the vehicle is captured using a high resolution photographic camera. To understand the variations in settings like exposure, frame aperture etc, we have choosen 3 cameras. Canon 1000D High resolution DSLR camera. HD images. Canon PowerShot IS MP digital camera with Image Stabilization. Nokia E72 5 MP digital camera embedded on a mobile phone.
37 Preprocessing Design Licensing Two operations involved are: 1.Resize Image from the camera is to be resized for optimization reasons. 2.Change color space Image is converted to Grayscale from RGB.
38 Localization Design Licensing Threshold is an image processing operation by which the pixels of the image are truncated to two values depending upon the value of threshold. We use this operation to convert the image to binary and localize the license plate from the image of the vehicle.
39 Localization Design Licensing Threshold is an image processing operation by which the pixels of the image are truncated to two values depending upon the value of threshold. We use this operation to convert the image to binary and localize the license plate from the image of the vehicle.
40 Design Licensing Thresholding requires pre image analysis for identifying the suitable threshold value. Many statistical and physical modelling algorithms have been developed for the same purpose. Normal thresholding techniques are inefficient due to several reasons. Hence, adaptive thresholding is used. To be precise, Otsu thresholding is used.
41 Design Licensing Thresholding requires pre image analysis for identifying the suitable threshold value. Many statistical and physical modelling algorithms have been developed for the same purpose. Normal thresholding techniques are inefficient due to several reasons. Hence, adaptive thresholding is used. To be precise, Otsu thresholding is used.
42 Design Licensing Thresholding requires pre image analysis for identifying the suitable threshold value. Many statistical and physical modelling algorithms have been developed for the same purpose. Normal thresholding techniques are inefficient due to several reasons. Hence, adaptive thresholding is used. To be precise, Otsu thresholding is used.
43 Connected Components Design Licensing Connected component analysis is performed to identify the characters in the image. Basic idea is to traverse through the image and find the connected pixels. Label them and extract. cvblobslib is a library under OpenCV which extract 8-connected components in binary or grayscale images.
44 Connected Components Design Licensing Connected component analysis is performed to identify the characters in the image. Basic idea is to traverse through the image and find the connected pixels. Label them and extract. cvblobslib is a library under OpenCV which extract 8-connected components in binary or grayscale images.
45 Connected Components Design Licensing Connected component analysis is performed to identify the characters in the image. Basic idea is to traverse through the image and find the connected pixels. Label them and extract. cvblobslib is a library under OpenCV which extract 8-connected components in binary or grayscale images.
46 Segmentation Design Licensing Crop out the labelled connected components called blobs. Save them as individual images.
47 Segmentation Design Licensing Crop out the labelled connected components called blobs. Save them as individual images.
48 Character recognition Design Licensing The blobs are send to an Optical Character Recognition engine for returning the ASCII. Tesseract is a leading OCR library developed in the HP Labs, later acquired and highly modified by Google. Google released this into the open source community.
49 Character recognition Design Licensing The blobs are send to an Optical Character Recognition engine for returning the ASCII. Tesseract is a leading OCR library developed in the HP Labs, later acquired and highly modified by Google. Google released this into the open source community.
50 License Design Licensing The system will be released under the GNU General Public License (GPL).
51 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
52 Standardization Image Quality Complexity and probability of failure of the system increases as there are multiple issues.
53 Standardization Image Quality Flaw in license plate standardization Dimensions Fonts type, size Art works Colours Position of the plate
54 Thresholding Standardization Image Quality Colour spaces Camera Lighting
55 Blob Classification Standardization Image Quality Undesirable blobs creep in during connected component analysis which if un-noticed can cause trouble in the character recognition phase.
56 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
57 Language Libraries Others The entire system is implemented using free software. Ubuntu GNU/Linux operating system is used.
58 Python Language Libraries Others Python is an interactive, interpreted, dynamic language which is free and highly efficient. Python is language libre. Shaped by the users around the world. Attracting more developers due to its simplicity. The entire ALPR system is implemented in Python
59 Libraries Language Libraries Others Open Computer Vision OpenCV library is developed at the laboratories of Intel Corporation. They contain sets of highly efficient multimedia processing functions. Python Imaging Library PIL is the base image processing library from Python.
60 Libraries Language Libraries Others Open Computer Vision OpenCV library is developed at the laboratories of Intel Corporation. They contain sets of highly efficient multimedia processing functions. Python Imaging Library PIL is the base image processing library from Python.
61 Libraries Language Libraries Others Tesseract OCR Tesseract OCR library from Google is used as the OCR engine. Developed at HP between 1984 and The code is available at Qt Designer and PyQt The GUI is designed using Qt designer and Python code for the same generated using PyQt. Qt is an extensive GUI library developed at Trolltech Inc.
62 Libraries Language Libraries Others Tesseract OCR Tesseract OCR library from Google is used as the OCR engine. Developed at HP between 1984 and The code is available at Qt Designer and PyQt The GUI is designed using Qt designer and Python code for the same generated using PyQt. Qt is an extensive GUI library developed at Trolltech Inc.
63 Other Language Libraries Others Project Management Subversion Source code control system. is very powerful, very usable, and very flexible, free software version control system. Document Generation LaTeX is a document preparation system created by Prof. Donald Knuth
64 Other Language Libraries Others Project Management Subversion Source code control system. is very powerful, very usable, and very flexible, free software version control system. Document Generation LaTeX is a document preparation system created by Prof. Donald Knuth
65 Language Libraries Others Diagram Generation Dia is a free Diagram editor software. With templates for drawing DFD s, UML s etc.
66 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
67 Otsu Thresholding Blob Classification Following algorithms have been used: Otsu thresholding technique. A linear-time component labeling using contour tracing technique. Blob Classification.
68 Otsu Thresholding Otsu Thresholding Blob Classification Adaptive thresholding. Considers that the image contains two classes of pixels (e.g. foreground and background). Introduced by Nobuyuki Otsu. Implemented using CV THRESHOLD OTSU Method of OpenCV.
69 Otsu Thresholding Otsu Thresholding Blob Classification Adaptive thresholding. Considers that the image contains two classes of pixels (e.g. foreground and background). Introduced by Nobuyuki Otsu. Implemented using CV THRESHOLD OTSU Method of OpenCV.
70 Otsu Thresholding Otsu Thresholding Blob Classification Adaptive thresholding. Considers that the image contains two classes of pixels (e.g. foreground and background). Introduced by Nobuyuki Otsu. Implemented using CV THRESHOLD OTSU Method of OpenCV.
71 Otsu Thresholding Otsu Thresholding Blob Classification Adaptive thresholding. Considers that the image contains two classes of pixels (e.g. foreground and background). Introduced by Nobuyuki Otsu. Implemented using CV THRESHOLD OTSU Method of OpenCV.
72 Blob Classification Otsu Thresholding Blob Classification Unwanted and noisy blobs may occur, which should be carefully removed. Step 1 Cropping Step 2 Compares aspect ratio with every other blobs. Step 3 Pixel coordinates are choosen to match the area of the plate.
73 Blob Classification Otsu Thresholding Blob Classification Unwanted and noisy blobs may occur, which should be carefully removed. Step 1 Cropping Step 2 Compares aspect ratio with every other blobs. Step 3 Pixel coordinates are choosen to match the area of the plate.
74 Blob Classification Otsu Thresholding Blob Classification Unwanted and noisy blobs may occur, which should be carefully removed. Step 1 Cropping Step 2 Compares aspect ratio with every other blobs. Step 3 Pixel coordinates are choosen to match the area of the plate.
75 Outline Design Licensing 5 Standardization Image Quality 6 Language Libraries Others 7 Otsu Thresholding Blob Classification 8
76 Performance Accuracy 1 Test for Performance. 2 Test for Accuracy.
77 Performance Performance Accuracy Image size has been identified to be the key element.
78
79 Accuracy Performance Accuracy Larger images leave perfect recognizable blobs.
80
81 Performance Accuracy Thankyou! Dept. of CSE MES College of Engineering...
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