Recognition of Isolated Printed Tifinagh Characters

Oujaoura, Mustapha; Minaoui, Brahim; Fakir, Mohamed; Ayachi, Rachid El; Bencharef, Omar

doi:10.5120/14802-3005

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…A GIST descriptor was used to extract features from the converted images. A GIST descriptor is a holistic filter for an image, it provides a low dimensional image with some information to understand the view in an image [60]. Three types of classification algorithms-KNN, decision tree, and random forest (RF)-were utilized.…”

Section: Volume XX 2020mentioning

confidence: 99%

Comprehensive Review of Cybercrime Detection Techniques

et al. 2020

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Cybercrimes describe cases of indictable offences and misdemeanours in which computer or any communication tools are involved as targets, commission instruments, incidental to, or that cases are associated with the prevalence of computer technology. Common forms of cybercrimes could be child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing and other cyber hacking. These kinds of cybercrimes are mostly leading to breaching users' privacy, security violation, business loss, financial fraud, or damage in public as well as government properties. Hence, this paper intensively reviews cybercrime detection and prevention methods. It first explores the different types of cybercrimes then discusses their threats against privacy and security in computer systems. It also describes the strategies that cybercriminals might utilize in committing these crimes against individuals, organizations, and societies. The paper then reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyses the vulnerabilities of each technique. As a future study, the paper provides recommendations for the development of cybercrime detection model in which it is capable to effectively detect cybercrime in comparison to the existing techniques.

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Section: Volume XX 2020mentioning

confidence: 99%

Comprehensive Review of Cybercrime Detection Techniques

et al. 2020

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“…Recent proposed approaches for the recognition of printed Tifinagh characters are generally based on the hybridization of several descriptors [3,4], classifiers [5,6], or both [7]; which gives very good results in terms of recognition rate. In return, combining several descritpors / classifiers increases significantly the compexity of the system.…”

Section: Fig 1: Tifinagh Of the Royal Institute Of Amazigh Culture Imentioning

confidence: 99%

Tifinagh Character Recognition using Harris Corner Detector and Graph Representation

Ouadid¹,

Boutaounte²,

Fakir³

et al. 2016

IJCA

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In this article, a printed Tifinagh character recognition system is presented. It is composed of three main steps: preprocessing, features extraction and classification. In the preprocessing step, binarization, normalization and thinning are applied to enhance the quality of the image. In the feature extraction step, corners are extracted using Harris Corner Detection method then represented by graph. In the classification step, graphs are compared using a graph matching method. Experimental results are obtained using IRCAM database to test the effectiveness. The system shows good results in terms of accuracy.

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“… The Amazigh language is spoken by about 30 million people in North Africa (the oasis of Siwa in Egypt, Morocco through Libya, Tunisia, Algeria, Niger, Mali, Burkina Faso and Mauritania) [3,4] . Due to the diversity of hand writing characters, there are two big approaches in this field and both need a dataset to be executed: the first one is based on complex classifiers like Artificial Neural Network or Support Vector Machine; those classifiers need a dataset to be trained to classify characters [5] . The other approaches also need a dataset this time to find a normalization of each character.…”

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confidence: 99%

Data set for Tifinagh handwriting character recognition

et al. 2015

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The Tifinagh alphabet-IRCAM is the official alphabet of the Amazigh language widely used in North Africa [1]. It includes thirty-one basic letter and two letters each composed of a base letter followed by the sign of labialization. Normalized only in 2003 (Unicode) [2], ICRAM-Tifinagh is a young character repertoire. Which needs more work on all levels. In this context we propose a data set for handwritten Tifinagh characters composed of 1376 image; 43 Image For Each character. The dataset can be used to train a Tifinagh character recognition system, or to extract the meaning characteristics of each character.

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Recognition of Isolated Printed Tifinagh Characters

Cited by 14 publications

References 21 publications

Comprehensive Review of Cybercrime Detection Techniques

Comprehensive Review of Cybercrime Detection Techniques

Tifinagh Character Recognition using Harris Corner Detector and Graph Representation

Data set for Tifinagh handwriting character recognition

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