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An End-To-End Efficient License Plate Detection and Recognition System using Deep Learning

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dc.contributor.author Bristi, Nushrat Jahan
dc.date.accessioned 2026-06-24T09:38:45Z
dc.date.available 2026-06-24T09:38:45Z
dc.date.issued 2025-01-13
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17379
dc.description Thesis report en_US
dc.description.abstract This research presents an enhanced license plate recognition system for real-time detection and recognition in transportation and security applications. YOLO object detection algorithms (YOLOv8s, YOLOv8x, YOLOv11s) enable accurate license plate localization, while EasyOCR ensures reliable alphanumeric identification in challenging situations, including low light and complex backgrounds. Testing on diverse datasets demonstrated high accuracy, with YOLOv11 and data augmentation achieving a peak F1 score of 98%. The system also addresses Bengali character recognition challenges, offering a foundation for region-specific improvements. These outcomes validate the system's effectiveness for law enforcement, traffic management and security. en_US
dc.description.sponsorship Daffodil International University en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Transportation en_US
dc.subject Security Applications en_US
dc.subject License Plate Recognition en_US
dc.subject Real-Time Detection en_US
dc.subject Alphanumeric Identification en_US
dc.title An End-To-End Efficient License Plate Detection and Recognition System using Deep Learning en_US
dc.type Thesis en_US


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