Multilanguage Number Plate Detection using Convolutional Neural Networks

TL;DR

This paper introduces a novel CNN-based approach using YOLOv2 and ResNet for highly accurate, language and layout independent number plate detection and classification, advancing international vehicle recognition technology.

Contribution

The paper proposes a new CNN architecture combined with YOLOv2 and ResNet for precise, multilingual, and layout-independent number plate detection and classification.

Findings

Detection precision of 99.57%

Classification accuracy of 99.33%

Outperforms previous methods in accuracy

Abstract

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.