Multilanguage Number Plate Detection using Convolutional Neural Networks
This addresses the need for precise, language-independent number plate detection for applications like vehicle tracking, though it appears incremental as it builds on existing object detection methods.
The paper tackles the problem of detecting and classifying number plates across different states, languages, and designs by proposing a new strategy using YOLOv2 with ResNet for detection and a novel CNN for classification, achieving average precisions of 99.57% for detection and 99.33% for classification.
Object Detection is a popular field of research for recent technologies. In recent years, profound learning performance attracts the researchers to use it in many applications. Number plate (NP) detection and classification is analyzed over decades however, it needs approaches which are more precise and state, language and design independent since cars are now moving from state to another easily. In this paperwe suggest a new strategy to detect NP and comprehend the nation, language and layout of NPs. YOLOv2 sensor with ResNet attribute extractor heart is proposed for NP detection and a brand new convolutional neural network architecture is suggested to classify NPs. The detector achieves average precision of 99.57% and country, language and layout classification precision of 99.33%. The results outperforms the majority of the previous works and can move the area forward toward international NP detection and recognition.