Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks

Salemdeeb M., ERTÜRK S.

ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, vol.10, no.4, pp.5979-5985, 2020 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 10 Issue: 4
  • Publication Date: 2020
  • Journal Name: ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH
  • Journal Indexes: Emerging Sources Citation Index (ESCI)
  • Page Numbers: pp.5979-5985
  • Kocaeli University Affiliated: Yes

Abstract

Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multinational and multi-language detection, with either one-line or two-line layouts is presented. The YOLOv2 detector with ResNet feature extraction core was utilized for LP detection, and a new low complexity convolutional neural network architecture was proposed to classify LPs. Results show that the proposed approach achieves an average detection precision of 99.57%, whereas the country, language, and layout classification accuracy is 99.33%.