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.

翻译：汽车及其他大型重型车辆的广泛使用促使了高效停车基础设施的发展。此外，车牌检测与识别算法常在全球范围内用于车辆识别，在标准化车牌尺寸和字体的地区，识别过程得以简化。据此，可将这两类数据整合以开发智能停车系统，该系统聚焦自动车牌识别技术。从输入的车牌图像中提取字符是自动车牌识别的核心目标，但其成本较高。本文提出Chaurah——一种专为停车设施设计、基于树莓派3的低成本自动车牌识别系统。该系统采用双阶段方法：第一阶段为自动车牌识别系统，利用两个卷积神经网络——主网络定位并识别车辆图像中的车牌，辅网络则执行光学字符识别以提取车牌上的独立字符。整个解决方案的第二部分是一个基于Flutter与Firebase构建的应用程序，用于数据库管理和车牌记录比对。该应用程序还作为基于停车时长的计费机制的用户界面，实现了本研究的全栈软件部署。