Chaurah: A Smart Raspberry Pi based Parking System
Soumya Ranjan Choudhaury Aditya Narendra Ashutosh Mishra Ipsit Misra
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.