Automatic Number Plate Recognition (A.N.P.R)

Automatic Number Plate Recognition (A.N.P.R)

Devom Jaiswal, Harsh Ladda, Sushma Addagatla, Rhea Patankar

Department of computer Engineering, PVPIT

Abstract— In an era marked by rapid urbanization and the proliferation of vehicles, efficient management of traffic flow and enhanced security measures have become paramount concerns for communities worldwide. To address these growing challenges, the use of Automatic Number Plate Recognition (ANPR) systems has become an indispensable tool. ANPR systems, also known as License Plate Recognition (LPR) systems, utilize advanced image processing, pattern recognition, and machine learning algorithms to automatically detect, segment, and recognize license plates from images or video streams captured by surveillance cameras or other sensors.

Furthermore, ANPR devices can only recognise horizontal number plates, making tilted plates impossible to identify. In this study, we provide various methods for overcoming these difficulties. The system recognises the number on the plate and compares it to a database to extract the owner's information. Pytesseract and TensorFlow were used to localise the plate and extract text.

With their exceptional versatility, ANPR systems are a reliable and effective solution for enhancing traffic management and improving security measures

Keywords— Automatic Number Plate Recognition (ANPR), Tensorflow, OCR (optical character recognition) ,Pytesseract, , K Nearest Neighbour (KNN), Support Vector Machine (SVM) Processing, Localizing , Character Segmentation and text extraction, Template Matching