ItemConference Paper

Application of Advanced Data Mining and Computer Vision Techniques in License Plate Recognition

Title
Application of Advanced Data Mining and Computer Vision Techniques in License Plate Recognition
Creator
Alase, Ayorinde; Multani, Danish; Amajala, Sai Rithwik; Udhaya, S.K.; Upreti, Kamal
Description
As urbanization is expanding rapidly and vehicular traffic is on the rise, efficient and automated vehicle identification is a must. Smart transportation, safety monitoring, & police work all heavily rely on Automatic License Plate Detection (ALPD). Traditional heuristic-based image processing techniques are incapable of handling environmental variations; Artificial intelligence (AI) & machine vision solutions are therefore used. YOLO, Faster R-CNN, and SSD are some of the most effective CNN-based and object identification algorithms that demonstrate cutting-edge accuracy in license plate recognition. The paper studies the usage of deep learning algorithms with prepossessing and advanced localization methods for ALPDs' optical character recognition (OCR) and character segmentation. The research also studies integrating ALPD with edge computing and IoTs to develop real-time smart traffic solutions. It also examines machine learning techniques, deep learning innovations, and conventional methods, emphasizing how well various models perform in comparison in terms of accuracy, computational efficiency, and real-time processing power. By employing cutting-edge designs, this study seeks to increase the scalability and resilience of license plate recognition systems, which will support future urban development, security applications, as intelligent transportation management. 2025 IEEE.
Source
Proceedings - 4th International Conference on Smart Technologies, Communication and Robotics 2025, STCR 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Automatic License Plate Recognition (ALPR); Computer Vision; Convolution Neural Networks (CNNs); Deep Learning; Faster R-CNN; License Plate Detection; LSTM; SSD (Single Shot MultiBox Detector); YOLO (You Only Look Once)
Coverage
Alase A., University of Arkansas at Little Rock, Electrical and Computer Engineering, Arkansas, Little Rock, United States; Multani D., Ust Global Solutions, Haryana, Gurgaon, India; Amajala S.R., Computer Science and Engineering, India; Udhaya S.K., Easwari Engineering College, Computer Science and Engineering, Chennai, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-835035753-0;
Format
online
Language
English
Type
Conference paper

Citation

Alase, Ayorinde; Multani, Danish; Amajala, Sai Rithwik; Udhaya, S.K.; Upreti, Kamal, “Application of Advanced Data Mining and Computer Vision Techniques in License Plate Recognition,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26219.

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