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A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network

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@article{IJASEIT1824,
   author = {Jabril Ramdan and Khairuldin Omar and Mohammad Faidzul},
   title = {A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {2},
   year = {2017},
   pages = {625--631},
   keywords = {peaks; arabic character; segmentation; baseline; neural network.},
   abstract = {Many methods of segmentation using detection of segmentation points or where the location of segmentation points is expected before the segmentation process,  the validity of segmentation points is verified by using ANNs. In this paper apply a novel method to detect correctly of location segmentation points by detect of peaks with neural networks for Arabic word. This method employs baseline and peaks identification; where using two steps to segmenting text. Where peaks identification function is applied which at the subword segment level to frame the minimum and maximum peaks, and baseline detection. Where these two steps have led to the best result through the model depends on minimum peaks attained by utilising a stroke operator with a view to extracting potential points of segmentation, and determining the baseline procedure was developed to approximate the parameters. Where this method has yielded highly accurate positive results for Arabic characters’ segmentation with four kinds of handwritten datasets as AHDB, IFN-ENIT, AHDB-FTR and ACDAR. Earlier results showed that the use of EDMS to MLP_ANN gives better results than GLCM and MOMENT in different groups and gives results of EDMS features on MNN with an accuracy level of 95.09% classifier for IFN-ENIT set of data.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1824},
   doi = {10.18517/ijaseit.7.2.1824}
}

EndNote

%A Ramdan, Jabril
%A Omar, Khairuldin
%A Faidzul, Mohammad
%D 2017
%T A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network
%B 2017
%9 peaks; arabic character; segmentation; baseline; neural network.
%! A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network
%K peaks; arabic character; segmentation; baseline; neural network.
%X Many methods of segmentation using detection of segmentation points or where the location of segmentation points is expected before the segmentation process,  the validity of segmentation points is verified by using ANNs. In this paper apply a novel method to detect correctly of location segmentation points by detect of peaks with neural networks for Arabic word. This method employs baseline and peaks identification; where using two steps to segmenting text. Where peaks identification function is applied which at the subword segment level to frame the minimum and maximum peaks, and baseline detection. Where these two steps have led to the best result through the model depends on minimum peaks attained by utilising a stroke operator with a view to extracting potential points of segmentation, and determining the baseline procedure was developed to approximate the parameters. Where this method has yielded highly accurate positive results for Arabic characters’ segmentation with four kinds of handwritten datasets as AHDB, IFN-ENIT, AHDB-FTR and ACDAR. Earlier results showed that the use of EDMS to MLP_ANN gives better results than GLCM and MOMENT in different groups and gives results of EDMS features on MNN with an accuracy level of 95.09% classifier for IFN-ENIT set of data.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1824
%R doi:10.18517/ijaseit.7.2.1824
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 2
%@ 2088-5334

IEEE

Jabril Ramdan,Khairuldin Omar and Mohammad Faidzul,"A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 2, pp. 625-631, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.2.1824.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ramdan, Jabril
AU  - Omar, Khairuldin
AU  - Faidzul, Mohammad
PY  - 2017
TI  - A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 2
Y2  - 2017
SP  - 625
EP  - 631
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - peaks; arabic character; segmentation; baseline; neural network.
N2  - Many methods of segmentation using detection of segmentation points or where the location of segmentation points is expected before the segmentation process,  the validity of segmentation points is verified by using ANNs. In this paper apply a novel method to detect correctly of location segmentation points by detect of peaks with neural networks for Arabic word. This method employs baseline and peaks identification; where using two steps to segmenting text. Where peaks identification function is applied which at the subword segment level to frame the minimum and maximum peaks, and baseline detection. Where these two steps have led to the best result through the model depends on minimum peaks attained by utilising a stroke operator with a view to extracting potential points of segmentation, and determining the baseline procedure was developed to approximate the parameters. Where this method has yielded highly accurate positive results for Arabic characters’ segmentation with four kinds of handwritten datasets as AHDB, IFN-ENIT, AHDB-FTR and ACDAR. Earlier results showed that the use of EDMS to MLP_ANN gives better results than GLCM and MOMENT in different groups and gives results of EDMS features on MNN with an accuracy level of 95.09% classifier for IFN-ENIT set of data.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1824
DO  - 10.18517/ijaseit.7.2.1824

RefWorks

RT Journal Article
ID 1824
A1 Ramdan, Jabril
A1 Omar, Khairuldin
A1 Faidzul, Mohammad
T1 A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 2
YR 2017
SP 625
OP 631
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 peaks; arabic character; segmentation; baseline; neural network.
AB Many methods of segmentation using detection of segmentation points or where the location of segmentation points is expected before the segmentation process,  the validity of segmentation points is verified by using ANNs. In this paper apply a novel method to detect correctly of location segmentation points by detect of peaks with neural networks for Arabic word. This method employs baseline and peaks identification; where using two steps to segmenting text. Where peaks identification function is applied which at the subword segment level to frame the minimum and maximum peaks, and baseline detection. Where these two steps have led to the best result through the model depends on minimum peaks attained by utilising a stroke operator with a view to extracting potential points of segmentation, and determining the baseline procedure was developed to approximate the parameters. Where this method has yielded highly accurate positive results for Arabic characters’ segmentation with four kinds of handwritten datasets as AHDB, IFN-ENIT, AHDB-FTR and ACDAR. Earlier results showed that the use of EDMS to MLP_ANN gives better results than GLCM and MOMENT in different groups and gives results of EDMS features on MNN with an accuracy level of 95.09% classifier for IFN-ENIT set of data.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1824
DO  - 10.18517/ijaseit.7.2.1824