Automatic Number Plate Recognition for indian standard number plates

Abstract

The purpose of this research is to demonstrate the application of decision analysis (DA) techniques to the decisions made throughout the lifecycle of Automatic Target Recognition (ATR) technology development. This work is accomplished in the hopes of improving the means by which ATR technologies are evaluated. The first step in this research was to create a flexible decision analysis framework that could be applied to a variety of decisions across several different ATR programs evaluated by the Comprehensive ATR Scientific Evaluation (COMPASE) Center. For the purpose of this research, a single COMPASE Center representative provided the value, utility, and preference functions for the DA framework. THe DA framework employs performance measures collected during ATR classification system (CS) testing to calculate value and utility scores. The authors gathered data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) program to demonstrate how the decision framework could be used to evaluate three different ATR CSs. A decision-maker may use the resultant scores to gain insight into any of the decisions that occur throughout the lifecycle of ATR technologies. Additionally, a means of evaluating ATR CS self-assessment ability is presented. Classification of this page unclassified Classification of Abstract unclassified Limitation of Abstract UU Number of Pages 38 ii Theviewsexpressedinthisarticlearethoseoftheauthorsanddonotreflectthe officialpolicyorpositionoftheUnitedStatesAirForce,DepartmentofDefense,or theU.S.Government. iii Abstract Thepurposeofthisresearchistodemonstratetheapplicationofdecisionanalysis

Automatic Number Plate Recognition System using Raspberry Pi

“…Number Plate Recognition is the process which uses the extracting method for recognizing the characters on the number plate. License plates in India generally may be black characters printed on either white or yellow plate [1] . So, the classification is based on the two types of Indian plates [2].…”

Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching

“…The best possible number plate location is found out by comparing width by height factor of actual Indian number plates to the same factor of plate like areas found by this method. The system shows maximum efficiency when the width by height factor is set between 3 and 7 [13]. The gray level plate images are enhanced by applying contrast extension and median filtering techniques [2].…”

“…In the last stage, characters are segmented from the Number Plate so that only useful information is retained for recognition where the image format will be converted into characters [1].Various research journals were consulted to find relevant information regarding ANPR based applications. ANPR systems are based on common approaches like Artificial Neural Network (ANN) [2,3],Probabilistic neural network (PNN) [4], Optical Character Recognition (OCR) [5,3], MATLAB [6], Configurable method [7], Sliding Concentrating window (SCW), Back-Propagation Neural Network [8], Support Vector Machine (SVM) [9], Inductive Learning [10], Regionbased, Color Segmentation [11] and Fuzzy-Based Algorithm [12], Scale Invariant Feature Transform (SIFT) [13].…”