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DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
NEURAL NETWORK BASED VEHICLE LICENSE PLATE RECOGNITION SYSTEM
PRESENTED BY
K.RAMAKRISHNAN
III YEAR CSE
ramky24@gmail.com
9042213575
N.M.SREENIVASAN
III YEAR CSE
sreeni02terror@gmail.com
9677635743
ABSTRACT:
This paper presents a neural network based system that is able to analyse the image of a car given by a camera to locate the registration plate, recognise and validate the registration number of the car. A novel image segmentation technique called Sliding Concentric Windows (SCW) is discussed. In the process of license plate recognition, the Region of Interest is detected by SCW segmentation, image masking, binarisation and connected component labeling arranged in sequence. Then, the detected region is segmented and forwarded to a Neural Network based Optical Character Recognition (OCR) engine. A three-layer Probabilistic Neural Network (PNN) of topology 108-180-36 with supervised training is used for the task of character recognition. After recognition, the characters are fed into a character validation module that determines whether the character is sufficiently valid based on the validation threshold provided as a parameter. The limitations such as image blurs, insufficient lighting conditions and arbitrary size of license plates and the scope of the system are also discussed.
INTRODUCTION:
Automatic vehicle license plate recognition systems are mass surveillance methods that use optical character recognition (OCR) on images to read the license plates on vehicles. License plate remains the principal vehicle identifier despite the fact that it can be deliberately altered in fraud situations or replaced with a counterfeit one (e.g., with a stolen plate). Recently, systems can scan number plates at around one per second on cars travelling up to 160 km/h. They use existing closed-circuit television (CCTV) or road-rule enforcement cameras, or ones
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
NEURAL NETWORK BASED VEHICLE LICENSE PLATE RECOGNITION SYSTEM
PRESENTED BY
K.RAMAKRISHNAN
III YEAR CSE
ramky24@gmail.com
9042213575
N.M.SREENIVASAN
III YEAR CSE
sreeni02terror@gmail.com
9677635743
ABSTRACT:
This paper presents a neural network based system that is able to analyse the image of a car given by a camera to locate the registration plate, recognise and validate the registration number of the car. A novel image segmentation technique called Sliding Concentric Windows (SCW) is discussed. In the process of license plate recognition, the Region of Interest is detected by SCW segmentation, image masking, binarisation and connected component labeling arranged in sequence. Then, the detected region is segmented and forwarded to a Neural Network based Optical Character Recognition (OCR) engine. A three-layer Probabilistic Neural Network (PNN) of topology 108-180-36 with supervised training is used for the task of character recognition. After recognition, the characters are fed into a character validation module that determines whether the character is sufficiently valid based on the validation threshold provided as a parameter. The limitations such as image blurs, insufficient lighting conditions and arbitrary size of license plates and the scope of the system are also discussed.
INTRODUCTION:
Automatic vehicle license plate recognition systems are mass surveillance methods that use optical character recognition (OCR) on images to read the license plates on vehicles. License plate remains the principal vehicle identifier despite the fact that it can be deliberately altered in fraud situations or replaced with a counterfeit one (e.g., with a stolen plate). Recently, systems can scan number plates at around one per second on cars travelling up to 160 km/h. They use existing closed-circuit television (CCTV) or road-rule enforcement cameras, or ones
References: 1. Haykin,Simon (2001) Neural Networks: A Comprehensive Foundation. 2nd Ed.New Delhi: Pearson 2 3. Cohen,Haim and Bergman,Gadi (2002) Car License Plate Recognition. [available at: http://visl.technion.ac.il/projects/2002w02/] [viewed on 17/08/2008] 4