• S. Penchala Reddy
• K. Nithya
• T. RamKumar
• R. Hazarathaiah
• K. Hemanth

Automatic License Plate Recognition (ALPR) is a crucial technology for intelligent transportation systems, enabling applications such as traffic monitoring, toll collection, and law enforcement. Traditional ALPR systems often struggle with low-light conditions, image occlusions, and varying plate designs. To address these challenges, this project proposes a robust ALPR system that integrates the You Only Look Once version 8 (YOLOv8) deep learning model for license plate detection and Multi-Level Extended Local Binary Pattern (MLELBP) for feature extraction and texture analysis.

License plate detection, texture descriptor, challenging conditions, vehicle identification, image processing, feature extraction, MLELBP, YOLO V8,robust detection.

"An Efficient Texture Descriptor For The Detection of License Plates Under Difficult Conditions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.n177-n181, April-2025, Available :http://www.jetir.org/papers/JETIR2504D22.pdf

"An Efficient Texture Descriptor For The Detection of License Plates Under Difficult Conditions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppn177-n181, April-2025, Available at : http://www.jetir.org/papers/JETIR2504D22.pdf