Chaurah: A Smart Raspberry Pi based Parking System

Abstract

The widespread usage of cars and other large, heavy vehicles necessitates the development of an effective parking infrastructure. Additionally, algorithms for detection and recognition of number plates are often used to identify automobiles all around the world where standardized plate sizes and fonts are enforced, making recognition a effortless task. As a result, both kinds of data can be combined to develop an intelligent parking system centered on ANPR technology. Extraction of the license plate characters from a photo is the primary objective of ANPR. Typically, this procedure is expensive. In this work, we introduce Chaurah, a low-cost Raspberry-Pi 3 based ANPR system designed especially for parking facilities. The system uses two stages of technique, the first of which is an ANPR system that uses two convolutional neural networks (CNNs). The first one uses a vehicle image to find and recognize license plates, while the second one uses optical character recognition to extract the licence plate numbers. The second step of the solution consists of a user-facing application made with Flutter and Firebase for database management in order to compare licence plate records. The app also functions as an interface for a payment system depending on the length of parking time, making it a full software embodiment of the concept.