Abstract: Automatic License plate recognition (ALPR) is a technology which recognizes the registration plate or number plate or License plate of a vehicle. In this paper, an Indian vehicle number plate is mined and the characters are predicted in efficient manner. ALPR involves four major technique i) Pre-processing ii) License Plate Location Identification iii) Individual Character Segmentation iv) Character Recognition. The opening phase, named pre-processing helps to remove noises and enhances the quality of the image using the conception of Morphological Operation and Image subtraction. The second phase, the most puzzling stage ascertain the location of license plate using the protocol Canny Edge detection, dilation and erosion. In the third phase, each characters characterized by Connected Component Approach (CCA) and in the ending phase, each segmented characters are conceptualized using cross correlation template matching- a scheme specifically appropriate for fixed format. Major application of ALPR is Tolling collection, Border Control, Parking, Stolen cars, Enforcement, Access Control, Traffic control. The database consists of 500 car images taken under dissimilar lighting condition is used. The efficiency of the system is 97%. Our future focus is Indian Vehicle License Plate Validation (Whether License plate of a vehicle is as per Road transport and highway standard).
Abstract: Automatic Vehicle Identification (AVI) has many
applications in traffic systems (highway electronic toll collection, red
light violation enforcement, border and customs checkpoints, etc.).
License Plate Recognition is an effective form of AVI systems. In
this study, a smart and simple algorithm is presented for vehicle-s
license plate recognition system. The proposed algorithm consists of
three major parts: Extraction of plate region, segmentation of
characters and recognition of plate characters. For extracting the
plate region, edge detection algorithms and smearing algorithms are
used. In segmentation part, smearing algorithms, filtering and some
morphological algorithms are used. And finally statistical based
template matching is used for recognition of plate characters. The
performance of the proposed algorithm has been tested on real
images. Based on the experimental results, we noted that our
algorithm shows superior performance in car license plate
recognition.
Abstract: The objective of this paper is to propose an adaptive multi threshold for image segmentation precisely in object detection. Due to the different types of license plates being used, the requirement of an automatic LPR is rather different for each country. The proposed technique is applied on Malaysian LPR application. It is based on Multi Layer Perceptron trained by back propagation. The proposed adaptive threshold is introduced to find the optimum threshold values. The technique relies on the peak value from the graph of the number object versus specific range of threshold values. The proposed approach has improved the overall performance compared to current optimal threshold techniques. Further improvement on this method is in progress to accommodate real time system specification.