Open AccessAutomatic Vehicle Number Plate Recognition System Using Machine Learning
Author(s) -
J. M. S. V. Ravi Kumar,
B. Sujatha,
N. Leelavathi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1074/1/012012
Subject(s) - license , artificial intelligence , computer science , computer vision , dilation (metric space) , pixel , mathematical morphology , process (computing) , segmentation , grayscale , edge detection , transformation (genetics) , pattern recognition (psychology) , image processing , image (mathematics) , mathematics , biochemistry , chemistry , combinatorics , gene , operating system
The number plate recognition (NPR) system is one of the categories of smart transportation and detection mechanism (STDM). This is a combination of the technology in which the application enables the system to detect and automatically read the license id of number plate of vehicle from digitally captured images. Automatically capturing the license plate is the process of detecting and transforming the pixels data of a digital image into the plain text data or ASCII text of the number plate. Our project contains a method for the vehicle number plate recognition from the image using mathematical morphological operations (erosion, dilation). The main objective is to use and combine different morphological operations in such a way that the license plate of the certain vehicle can be detected and translatedeffectively. This is based on various operation such as image improvement, Gray scale transformation, Bilateral Filtering edge detection and getting the number plate from the picture of vehicle. After the completion of the above-mentioned steps, now the process of segmentation is being applied to detect the text present on number plate by making use ofmatching of template and OCR. This system is able to detect the license number accurately as well as quickly from the vehicle’s picture.