Evaluation of Features Extraction and Classification Techniques for Offline Handwritten Tifinagh Recognition

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Mouhcine Rabi
Mouhcine Rabi
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Mustapha Amrouch
Mustapha Amrouch
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Zouhir Mahani
Zouhir Mahani

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Evaluation of Features Extraction and Classification Techniques for Offline Handwritten Tifinagh Recognition

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Abstract

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.

References

9 Cites in Article
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  2. S Arora1,D Bhattacharjee2,M Nasipuri2,D Basu2,M Kundu2 (2010). Application of Statistical Features in Handwritten Devnagari Character Recognition.
  3. S Angadi,H Sharanabasavaraj,Angadi (2015). STRUCTURAL FEATURES FOR RECOGNI-TION OF HAND WRITTEN KANNADA CHARA-CTER BASED ON SVM.
  4. Jawad Alkhateeb,Jinchang Ren,Jianmin Jiang,Stan Ipson,Haikal El Abed (2008). Word-based handwritten Arabic scripts recognition using DCT features and neural network classifier.
  5. Y Saady,Ali Rachidi,Mostafa Yassa,Driss Mammass (2011). AMHCD: A Database for Amazigh Handwritten Character Recognition Research.
  6. Mohamed Abaynarh,Lahbib Zenkouar (2015). Offline Handwritten Characters Recognition Using Moments Features and Neural Networks.
  7. B El Kessab,C Daoui,B Bouikhalene,R Salouan (2015). Handwriting Moroccan regions recognition using Tifinagh character.
  8. A Haidar,M Fakir,O (2012). BENCHAREF Hybridation des modèles de Markov cachés et de la logique floue pour la reconnaissance des caractères Tifinagh manuscrits. 5ème conférence internationale sur les TIC pour l'amazighe.
  9. Amrouch Mustapha (2012). Reconnaissance des caractères imprimés et manuscrits, textes et documents basés sur les modèles de Markov cachés.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

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): .

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Issue Cover
GJCST Volume 16 Issue C5
Pg. 37- 42
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
D.3.4,F.4.2
Version of record

v1.2

Issue date

January 27, 2017

Language
en
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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.

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Evaluation of Features Extraction and Classification Techniques for Offline Handwritten Tifinagh Recognition

Mouhcine Rabi
Mouhcine Rabi
Mustapha Amrouch
Mustapha Amrouch
Zouhir Mahani
Zouhir Mahani

Research Journals