A Novel Approach for Detecting Number Plate Based on Overlapping Window and Region Clustering for Indian Conditions

Abstract

Abstract-Automatic Number Plate Recognition (ANPR) is becoming very popular and topic of research for the Intelligent Transportation System (ITS). Many researchers are working in this direction, as it is the topic of interest. In proposed system, we have presented a novel approach for number plate (NP) detection, which can be useful for Indian conditions. The system works well in different illumination conditions and 24 hours manner. Experiments achieved excellent accuracy of 98.88% of overall accuracy of NP detection on 90 vehicle images with different conditions and captured at different timing during day and night. Out of these 90 images, 89 images were segmented successfully. The minimum image size was 800 X 600 pixels. The system was developed using the Microsoft .NET 3.5 framework and Visual Studio 2008 as IDE with the Intel core i3 2.13 GHz processor having 3 GB RAM. Other systems discussed in this paper reported better processing time of less than 1s, but some of these systems work under restricted conditions and accuracy is also not as good as our system.

MobileNet‐SSD MicroScope using adaptive error correction algorithm: real‐time detection of license plates on mobile devices

“…With the development of society, the number of vehicles in the world is growing rapidly, causing a lot of pressure on urban traffic. In order to improve the efficiency of controlling vehicles and alleviate road traffic pressure, vehicle and license plate detection technologies [1][2][3] have become the focus of attention. The technologies are utilised extensively, such as monitoring illegal vehicles [4], automatic parking charging [5] and so on, bringing huge economic benefits.…”