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. 2015 Jul 26:4:534-43.
doi: 10.1016/j.dib.2015.07.018. eCollection 2015 Sep.

Handwriting Moroccan regions recognition using Tifinagh character

B El Kessab  1 , C Daoui  1 , B Bouikhalene  1 , R Salouan  1
Affiliations

Handwriting Moroccan regions recognition using Tifinagh character

B El Kessab et al. Data Brief. .

Abstract

The territorial organization of Morocco during administratives division of 2009 is based on 16 regions. In this work we will create a system of recognition of handwritten words (names of regions) using the Amazigh language is an official language by the Moroccan Royal Institute of Amazigh Culture (IRCAM) (2003a) [1] such as this language is slightly treated by researchers in pattern recognition field that is why we decided to study this language (El Kessab et al., 2013 [3]; El Kessab et al., 2014 [4]) that knowing the state make a decision to computerize the various public sectors by this language. In this context we propose a data set for handwritten Tifinagh regions composed of 1600 image (100 Image for each region). The dataset can be used in one hand to test the efficiency of the Tifinagh region recognition system in extraction of characteristics significatives and the correct identification of each region in classification phase in the other hand.

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Figures

Fig. 1
Fig. 1
The proposed system for handwritten Tifinagh words recognition.
Fig. 2
Fig. 2
Comparison between the Tifinagh, Arabic and Latin characters.
Fig. 3
Fig. 3
Sixteen regions with Amazigh language.
Fig. 4
Fig. 4
Example of handwritten Tifinagh region from the proposed data base.
Fig. 5
Fig. 5
The graphical representation of recognition rate τr for each region.
Fig. 6
Fig. 6
The original image.
Fig. 7
Fig. 7
Graphical representation of segmentation column.
Fig.8
Fig.8
The segmentation in columns.
Fig. 9
Fig. 9
The square zoning method.
Fig. 10
Fig. 10
Processes of feature extraction by square zoning.
Fig. 11
Fig. 11
The triangular zoning method.
Fig. 12
Fig. 12
Processes of feature extraction by triangular zoning.
Fig. 13
Fig. 13
The determination of optimal hyperplane, vectors supports, maximum Marge and valid hyperplanes.
Fig. 14
Fig. 14
The multi-layer perceptron.
Fig. 15
Fig. 15
The graphical representation of recognition rate τr of each region with all methods.
Fig. 16
Fig. 16
The graphical representation of recognition rate τr of all feature extraction methods.
Fig. 17
Fig. 17
The graphical representation of recognition rate τr of square zoning method and SVM classifier.
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References

    1. Institut Royal de la Culture Amazighe (IRCAM). Proposition de codification des tifinaghes, Rabat, Morocco, 2003a.
    1. Morocco in Figures 2003: a document by the Moroccan Embassy in the USA.
    1. El Kessab B., Daoui C., Bouikhalene B. Handwritten Tifinagh text recognition using neural networks and hidden Markov models. Int. J. Comput. Appl. 2013;75(18):975–8887.
    1. El Kessab B., Daoui C., Bouikhalene B., Salouan. R. Some comparative studies or cursive handwritten Tifinagh characters recognition systems. Int. J. Hybrid Inform. Technol. 2014;7(6):295–306.
    1. Sadiqi Fatima. Vol. 2. Oxford University Press; 2003. The Teatchin of Tifinagh (Berber) in Morocco, Hand book of Language and Ethnic Identity, The Success-Failure Continuum in Language and Ethnic Identity Efforts; pp. 33–44.

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