Employing Deep Learning Approaches for Automatic License Plate Recognition: A Review
Publish place: 3rd International Conference on Soft Computing
Publish Year: 1398
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSCG03_160
تاریخ نمایه سازی: 14 فروردین 1399
Abstract:
Employing deep learning approaches has resulted in magnificent perfections in computer vision applications in recent years. In addition, Deep Neural Networks (DNNs) have shown to be remarkable alternatives for common shallow machine learning techniques like Support Vector Machines (SVM). Deep learning provides great solutions for both classic and modern image processing, feature extraction and object detection problems. Considering the advantages of utilizing DNNs in a wide range of computer vision fields, this paper presents a concise review of different deep learning approaches employed in Automatic License Plate Recognition (ALPR) systems. In such systems, deep learning techniques have been utilized in various phases of ALPR including license plate detection, character segmentation and Optical Character Recognition (OCR). Additionally, a comprehensive overview of common DNN architectures is introduced for better clarification and classification of introduced methods.
Keywords:
Deep Learning , Deep Neural Networks , Automatic License Plate Recognition , Intelligent Transportation Systems , Image Processing.
Authors
Sajjad Soroori
Department of Computer Engineering, University of Guilan, Rasht, Iran
Ali Tourani
Department of Computer Engineering, University of Guilan, Rasht, Iran
Asadollah Shahbahrami
Department of Computer Engineering, University of Guilan, Rasht, Iran