Deep Learning Based Vehicle Tracking in Traffic Management

Shehata, Mohamed; Abo-Al-Ez, Reda; Zaghlool, Farid; Abou-Kreisha, Mohamed Taha

doi:10.14445/22312803/ijctt-v67i3p102

Cited by 4 publications

(2 citation statements)

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“…Automatic Arabic License Plate Recognition (AALPR) systems can be classified into two main approaches: segmentation-based approach [19] and segmentation-free approach [19] & [20]. The segmentation-based approach segments individual license plate characters and then detects each character using an OCR model.…”

Section: B License Plate Recognitionmentioning

confidence: 99%

“…Sarfraz et al [13] performed character segmentation and normalization, then template matching with Arabic characters. Shehata et al [19] proposed four systems to recognize license plates. The first system is a segmentation-based approach and has three main modules: license plate extraction, character segmentation module, and character recognition module.…”

Section: B License Plate Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust License Plate Detection and Recognition Framework for Arabic Plates with Severe Tilt Angles

Hefnawy,

Lila,

Hemayed

et al. 2024

IJACSA

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This paper addresses the challenge of accurately detecting and recognizing Arabic license plates, particularly those subjected to severe tilt angles. It presents a robust license plate detection and recognition framework that consists three main steps: plate detection and segmentation, plate perspective correction, and vehicle number recognition. In the first step, a mask R-CNN model is used to detect the plate location, providing pixel-wise labels of identified plates' areas. Following this, a perspective correction technique is used to obtain a clear and rectangular image of each license plate in the image. Lastly, the framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for accurate vehicle number recognition. The framework's efficacy is demonstrated through its application to build a plate recognition system tailored for Egyptian license plates. The system was tested on a dataset collected from campus gate cameras at Zewail city of science and technology, achieving a character accuracy of 97%.

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Section: B License Plate Recognitionmentioning

confidence: 99%

Section: B License Plate Recognitionmentioning

confidence: 99%