A deep learning technique for vehicle license plate recognition / Ahmed Mohamed Elaraby ; Supervised Ammar Mohammed , Ahmed Hamza

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Ahmed Mohamed Elaraby

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TextLanguage: English Publication details: Cairo : Ahmed Mohamed Elaraby , 2021Description: 162 Leaves : charts , facsmilies , photographs ; 30cmOther title:

تقنية التعلم العميق للتعرف على لوحات ترخيص المركبات [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Information Systems Technology Summary: Automatic license plate recognition (ALPR) has been a frequent topic of research. However, most of the current ALPR research focuses on recognizing the English license plate with very little research on the Arabic license plate. In this thesis, we take a first step in training a deep learning-based model for Arabic license plate recognition. ALPR is one of the most important applications in video surveillance and computer vision. License plate (LP) recognition system commonly combines two sub-systems: LP detection which aims to locate the LP; and LP recognition which aims to recognize the characters in the LP. The conventional algorithms used in ALPR, for detection and recognition, are susceptible to multiple challenging conditions, such as variations in lighting, viewing angle or camera rotation as well as occlusion. For automatic Arabic license plates recognition (AALPR), most of the researchers in Egypt and Arabic countries have focused on recognizing characters in LP directly using many algorithms such as Sobel edge detection, histogram equalization or template matching assuming that characters have single-font, not rotated and fixed-size properties. However, with the massive power of deep learning (DL) that can greatly improve AALPR in terms of LP detection speed and LP characters recognition accuracy, even though the existence of more challenging conditions and without prior assumptions. This thesis presents a robust and efficient AALPR system based on the state-of-the-art You only look once (YOLO) object detector that is the most robust DL-based framework under different conditions (e.g., variations in camera, lighting, and background). For character segmentation and recognition, our system uses a convolutional neural network (CNN) to detect, segment and recognize characters within detected Arabic LP. This network used the Tiny-YOLOv3 architecture, as it is significantly faster and have slightly higher accuracy compared to the other reviewed architectures

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Holdings
Item type	Current library	Home library	Call number	Copy number	Status	Date due	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.18.07.M.Sc.2021.Ah.D (Browse shelf(Opens below))		Not for loan		01010110083519000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.18.07.M.Sc.2021.Ah.D (Browse shelf(Opens below))	83519.CD	Not for loan		01020110083519000

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Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Information Systems Technology

Automatic license plate recognition (ALPR) has been a frequent topic of research. However, most of the current ALPR research focuses on recognizing the English license plate with very little research on the Arabic license plate. In this thesis, we take a first step in training a deep learning-based model for Arabic license plate recognition. ALPR is one of the most important applications in video surveillance and computer vision. License plate (LP) recognition system commonly combines two sub-systems: LP detection which aims to locate the LP; and LP recognition which aims to recognize the characters in the LP. The conventional algorithms used in ALPR, for detection and recognition, are susceptible to multiple challenging conditions, such as variations in lighting, viewing angle or camera rotation as well as occlusion. For automatic Arabic license plates recognition (AALPR), most of the researchers in Egypt and Arabic countries have focused on recognizing characters in LP directly using many algorithms such as Sobel edge detection, histogram equalization or template matching assuming that characters have single-font, not rotated and fixed-size properties. However, with the massive power of deep learning (DL) that can greatly improve AALPR in terms of LP detection speed and LP characters recognition accuracy, even though the existence of more challenging conditions and without prior assumptions. This thesis presents a robust and efficient AALPR system based on the state-of-the-art You only look once (YOLO) object detector that is the most robust DL-based framework under different conditions (e.g., variations in camera, lighting, and background). For character segmentation and recognition, our system uses a convolutional neural network (CNN) to detect, segment and recognize characters within detected Arabic LP. This network used the Tiny-YOLOv3 architecture, as it is significantly faster and have slightly higher accuracy compared to the other reviewed architectures

Issued also as CD

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