Article Fingerprint
ReserarchID
CSTSDE5Y01A
This paper presents a review on different features extraction and classification methods for off-line handwritten Amazigh characters (called Tifinagh) recognition. The features extraction methods are discussed based on Statistical, Structural, Global transformation and moments.Although a number of techniques are available for feature extraction and classification,but the choice of an excellent technique decides the degree of accuracy of recognition. A series of experimentswere performed on AMHCD databaseallowing to evaluate the effectiveness of different techniques of extraction features based on Hidden Markov models, Neural network and Support vector Machine classifiers. The statistical techniques giveencouraging results.
Mouhcine Rabi. 2017. \u201cEvaluation of Features Extraction and Classification Techniques for Offline Handwritten Tifinagh Recognition\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C5): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.
Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.
Total Score: 123
Country: Morocco
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: Mouhcine Rabi, Mustapha Amrouch, Zouhir Mahani (PhD/Dr. count: 0)
View Count (all-time): 294
Total Views (Real + Logic): 6820
Total Downloads (simulated): 1793
Publish Date: 2017 01, Fri
Monthly Totals (Real + Logic):
This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
This paper presents a review on different features extraction and classification methods for off-line handwritten Amazigh characters (called Tifinagh) recognition. The features extraction methods are discussed based on Statistical, Structural, Global transformation and moments.Although a number of techniques are available for feature extraction and classification,but the choice of an excellent technique decides the degree of accuracy of recognition. A series of experimentswere performed on AMHCD databaseallowing to evaluate the effectiveness of different techniques of extraction features based on Hidden Markov models, Neural network and Support vector Machine classifiers. The statistical techniques giveencouraging results.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.