arXiv:2501.02270
공식 코드 · Papers with Code
Efficient Video-Based ALPR System Using YOLO and Visual Rhythm
원문 초록
Automatic License Plate Recognition (ALPR) involves extracting vehicle license plate information from image or a video capture. These systems have gained popularity due to the wide availability of low-cost surveillance cameras and advances in Deep Learning. Typically, video-based ALPR systems rely on multiple frames to detect the vehicle and recognize the license plates. Therefore, we propose a system capable of extracting exactly one frame per vehicle and recognizing its license plate characters from this singular image using an Optical Character Recognition (OCR) model. Early experiments show that this methodology is viable.
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Efficient License Plate Recognition in Videos Using Visual Rhythm and Accumulative Line Analysis
Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark
Embedding Similarity Guided License Plate Super Resolution
Advancing Vehicle Plate Recognition: Multitasking Visual Language Models with VehiclePaliGemma
Comparison of Image Preprocessing Techniques for Vehicle License Plate Recognition Using OCR: Performance and Accuracy Evaluation
MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories
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