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

dc.contributor.authorBristi, Nushrat Jahan
dc.date.accessioned2026-06-24T09:38:45Z
dc.date.available2026-06-24T09:38:45Z
dc.date.issued2025-01-13
dc.descriptionThesis report
dc.description.abstractThis 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.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17379
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17379
dc.language.isoen_US
dc.publisherDaffodil International University
dc.sourceDIU Institutional Repository
dc.subjectTransportation
dc.subjectSecurity Applications
dc.subjectLicense Plate Recognition
dc.subjectReal-Time Detection
dc.subjectAlphanumeric Identification
dc.titleAn End-To-End Efficient License Plate Detection and Recognition System using Deep Learning
dc.typeThesis

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
221-15-4806.pdf.txt
Size:
87.49 KB
Format:
Adobe Portable Document Format

Collections