Chaurah: A Smart Raspberry Pi based Parking System
This work addresses parking infrastructure inefficiencies for facility operators by proposing an incremental, cost-effective ANPR system.
The authors tackled the problem of creating a low-cost intelligent parking system by developing Chaurah, which uses a Raspberry Pi 3 and dual-stage convolutional neural networks for Automatic Number Plate Recognition (ANPR) and integrates with a Flutter and Firebase application for billing and management, resulting in a minimal-cost solution.
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 an effortless task. As a result, both kinds of data can be combined to develop an intelligent parking system focuses on the technology of Automatic Number Plate Recognition (ANPR). Retrieving characters from an inputted number plate image is the sole purpose of ANPR which is a costly procedure. In this article, we propose Chaurah, a minimal cost ANPR system that relies on a Raspberry Pi 3 that was specifically created for parking facilities. The system employs a dual-stage methodology, with the first stage being an ANPR system which makes use of two convolutional neural networks (CNNs). The primary locates and recognises license plates from a vehicle image, while the secondary performs Optical Character Recognition (OCR) to identify individualized numbers from the number plate. An application built with Flutter and Firebase for database administration and license plate record comparison makes up the second component of the overall solution. The application also acts as an user-interface for the billing mechanism based on parking time duration resulting in an all-encompassing software deployment of the study.