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 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.
Abstract (translated)
汽车和其他大型重型交通工具的广泛使用催生了有效的停车基础设施的发展。此外，通常使用算法来检测和识别车牌号码，以识别世界各地标准化车牌尺寸和字体的汽车，这使得识别变得轻松。因此，可以将这两种数据结合以开发专注于自动车牌识别技术（ANPR）的智能停车系统。从输入的車牌圖像中檢索字符是ANPR的唯一目的，而這是一個昂貴的過程。在本文中，我們提議Chaurah，一個最小成本的ANPR系統，該系統依賴於專門為停車場設計的Raspberry Pi 3。該系統采用雙階段方法，第一階段是使用兩個卷積神經網絡（CNN）的ANPR系統。主要從車輛圖像中檢測和識別車牌，而第二階段對車牌進行光學字符識別（OCR）以識別車牌上的個人化號碼。由Flutter和Firebase編寫的用於數據庫管理和車牌記錄比對的應用程序是整個解決方案的第二個組件。該應用程序還充当用戶界面，用於計費機制，這使得研究部署的全面性更加廣泛。
URL
https://arxiv.org/abs/2312.16894
