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.

本文提出了 Chaurah，这是一个针对停车设施而设计的低成本 ANPR 系统，它借助于树莓派 3 进行操作。系统使用了双阶段方法，第一阶段利用两个卷积神经网络 (CNNs) 从车辆图像中定位和识别车牌，而第二阶段则进行光学字符识别 (OCR) 以识别车牌上的个性化数字。同时，本研究还构建了一个基于 Flutter 和 Firebase 的应用程序用于数据库管理和车牌记录比对，并且作为用户界面用于基于停车时长的计费机制，实现了全方位的软件部署。