An Automatic Number Plate Recognition System under Image Processing

Abstract

Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases:-Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.

Theft Vehicle Identification and Tracking System

“…Ankita Yadav, Deepak Chaudhary used MATLABR2013 to run the programs and template matching for recognition of characters [3]. Sarbjit Kaur, Sukhvir Kaur designed an efficient approach for ANPR and found it to be tedious for processing multi-plates at a time [4]. Bhavin V Kakani, Divyang Gandhi, Sagar Jani proposed a system which was fast, accurate and robust using OCR algorithm [5].…”

“…Kulkarani Suraj Deeliprao, Pallavi.S.Deshpande proposed a system for Automatic Vehicle License Plate Recognition using fundamental image processing steps with MATLAB for theft detection. This proposed system has been designed using Connected Component Analysis, Boundary box analysis and Template matching [4].…”

El procesamiento de imágenes y su potencial aplicación en empresas con estrategia digital

“…Si el análisis se orienta a una procesamiento de aprendizaje de imágenes continuas (como el caso de un video), para métodos de aprendizaje de inteligencia artificial las técnicas de enfoque y nitidez de la captura cobran mayor relevancia (Kaur, 2016).…”

A Robust Zonal Fractal Dimension Method for the Recognition of Handwritten Telugu Digits

“…In transform domain Fourier transform (FT) [14][15][16], Gabor transform [17][18][19][20][21][22], Wavelet transform [23][24][25][26] are some for extracting features in the literature. Probabilistic neural network [27], template matching [65][66][67], Artificial neural network [28,29,56,68], Hidden Markov-model [30,31], support vector machine (SVM) [32], [11] are some classification techniques for recognizing numerals or scripts.…”