| 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 |