Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Alase, Ayorinde; Multani, Danish; Amajala, Sai Rithwik; Udhaya, S.K.; Upreti, Kamal Title A name given to the resource Application of Advanced Data Mining and Computer Vision Techniques in License Plate Recognition Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 4th International Conference on Smart Technologies, Communication and Robotics 2025, STCR 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/STCR62650.2025.11018987" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/STCR62650.2025.11018987</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008423484?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008423484?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource As urbanization is expanding rapidly and vehicular traffic is on the rise, efficient and automated vehicle identification is a must. Smart transportation, safety monitoring, &amp; 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) &amp; 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. Subject The topic of the resource 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) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-835035753-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online