Arabic Real-Time License Plate Recognition System

El-Said, Shaimaa Ahmed; Alharthi, Haifa; Alrubaia, Reem; Abutile, Sarah; Aljres, Rawan; Al-Anazi, Amal M.; Albrikan, Alanoud

doi:10.1007/978-3-030-36368-0_12

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…The segmentation-based approach segments individual license plate characters and then detects each character using an OCR model. Elsaid, et al [21] proposed a segmentation-based approach trained on Arabic and Indian numerals and Arabic alphabets on Saudi Arabian license plates. The proposed pipeline has five stages: license plate preprocessing, license plate localization, character segmentation, features extraction, and character recognition.…”

Section: B License Plate Recognitionmentioning

confidence: 99%

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%

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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show abstract

“…Research in the Arabic OCR domain has attracted tremendous interest in the past few years, mainly due to its challenging nature in electively satisfying both aims without degrading one another [11][12][13][14]. For example, the authors in [15] built an Arabic OCR system using Scale Invariant Feature Transform (SIFT) as features for classification of letters in conjunction with the online failure prediction method.…”

Section: Related Workmentioning

confidence: 99%

“…The main goal of the feature extraction stage is to maximize the recognition rate with the minimum number of features that are stored in a feature vector. The underlying idea of this stage is to extract features from word images that achieve a high degree of similarity among samples of the same classes and a high degree of variation among samples of other classes [6] [13]. As stated in [5], feature extraction methods based on second-order statistics achieved higher differentiation rates than the power spectrum (transform-based), and structural methods.…”

Section: Feature Extractionmentioning

confidence: 99%

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

Darwish

Elzoghaly

2020

IEEE Access

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In the recent few years, there was a concentrated search on Arabic Optical Character Recognition (OCR), especially the recognition of scanned, offline, machine-printed documents. However, Arabic OCR consequences are dissatisfying and are still a developed research area. Finding the best feature extraction techniques and selecting an appropriate classification algorithm lead to supreme recognition accuracy and low computational overhead. This paper presents a new Arabic OCR model by integrating both of Genetic Algorithm (GA) and the Fuzzy K-Nearest Neighbor classifier (F-KNN) in a unified framework to enhance the identification accuracy. GA is utilized as a feature selection algorithm that has better convergence and spread of solutions with candid variation preservation mechanism. The F-KNN algorithm is more appropriate to classify ambiguous or uncertain data objects in the sense that every object belongs to all classes with different degrees of membership. The suggested model semantically fuses bio-inspired based feature vectors with fuzzy KNN classifier to build accurate membership function for each class. Experimental results compared to other approaches revealed the effectiveness of the suggested model and demonstrated that the feature selection approach increased the identification accuracy process.

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“…There are several uses for OCR, like documentation recovery, postcode and vehicle license plate identification, and many more financial and commercial operations [1]. Online-OCR and Offline-OCR systems are the two main types of OCR.…”

Section: Introductionmentioning

confidence: 99%

OCR for Arabic character recognition with advanced principal component analysis based on feature extraction and fuzzy-KNN based classification

Fredrik

2022

ijhs

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Offline character recognition has become a highly important study field for various pattern recognition applications in recent years. Several handwritten character recognition systems have been suggested, with the complexity of these systems varying depending on the recognizing units' writing styles. In reality, identifying letters or numerals is far simpler than recognizing cursive sentences or lines of text. As a result, early handwriting recognition algorithms could only distinguish a few characters with limited vocabularies. Nowadays Arabic handwritten character recognition is very important as it is very difficult to identify. The cursive writing and variety of styles make this recognition more complex. This paper presents an automated model for ACR. This ACR is constructed from four phases: Preprocessing, Segmentation, Feature Extraction, and Classification. In this research article, we compare the advanced EPSO EKNN Algorithm with the earlier DBN Algorithm and also suggest a new method having higher Accuracy. In this research article, the "Enhanced K-Nearest Neighbor" (EKNN) classification model was used to identify and classify or simply recognize the particular Arabic character, and the "Extended Particle Swarm Optimization" (EPSO) methodology was introduced to select the best feature from feature extraction to comply with Arabic-Character Classification.

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Arabic Real-Time License Plate Recognition System

Cited by 5 publications

References 5 publications

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

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

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

OCR for Arabic character recognition with advanced principal component analysis based on feature extraction and fuzzy-KNN based classification

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