Determine characters by mathematical model for segmentation Arabic words by voronoi diagrams

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Abstract

Objectives: The objectives are to use a mathematical model to define a region-based segmentation method. This study determines whether the Connected Component (CC) is one or more than one character. Method: Whereas the other methods they tend to ignore the solid foundation of describing characters and connection points. This proposed method adopts on many stages for adaptive the mathematic in segmentation characters process are: i) peak detection from vertical histogram for (CC), and ii) enhancement of the model using a mathematical model to improve the segmentation method based on the Voronoi Diagram (VD) Through a number of peaks. Findings: Whereas characters, such as and , are confusing to segmentation methods; these errors include separating connection strokes from both sides to produce a separated one. Other errors must be handled at a later stage, such as segmenting the character at an acute angle. Whereas the mathematical model is depending on peaks, numbers, direction, and length of CC. This model is tested on segmentation using five Arabic datasets as: AHDB, IFN-ENIT, AHDB-FTR, APTI, Zeki and Al Hamad DB datasets. The Preliminary results show that the application of the EDMS feature with multi perceptron-NN classifier it's preferable. Its accuracy when compared with Zeki method is 96.81% for the ACTOR printed dataset and the rate of this method is 85.81% for Zeki dataset and also compared with Al Hamad method is 95.09%, and 89.10% for ACDARhandwrittendataset. Whereas the others datasets accuracies are 95.09% for IFN-ENIT, 98.27% for APTI, 91.63% for AHDB, and 90.69% for AHDB-FTR on same feature (EDMS) and classifier (MLP_NN). Novelty: Adapt Mathematics with segmentation process to determine whether the CC is one or more than one character. Using a mathematical model based on the VD to avoid over segmentation.

Original languageEnglish
Article number84801
JournalIndian Journal of Science and Technology
Volume9
Issue number40
DOIs
Publication statusPublished - 2016

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Mathematical models
Classifiers
Neural networks

Keywords

  • Arabic words
  • Mathematical model
  • More than one character
  • Segmentation
  • Voronoi diagrams

ASJC Scopus subject areas

  • General

Cite this

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title = "Determine characters by mathematical model for segmentation Arabic words by voronoi diagrams",
abstract = "Objectives: The objectives are to use a mathematical model to define a region-based segmentation method. This study determines whether the Connected Component (CC) is one or more than one character. Method: Whereas the other methods they tend to ignore the solid foundation of describing characters and connection points. This proposed method adopts on many stages for adaptive the mathematic in segmentation characters process are: i) peak detection from vertical histogram for (CC), and ii) enhancement of the model using a mathematical model to improve the segmentation method based on the Voronoi Diagram (VD) Through a number of peaks. Findings: Whereas characters, such as and , are confusing to segmentation methods; these errors include separating connection strokes from both sides to produce a separated one. Other errors must be handled at a later stage, such as segmenting the character at an acute angle. Whereas the mathematical model is depending on peaks, numbers, direction, and length of CC. This model is tested on segmentation using five Arabic datasets as: AHDB, IFN-ENIT, AHDB-FTR, APTI, Zeki and Al Hamad DB datasets. The Preliminary results show that the application of the EDMS feature with multi perceptron-NN classifier it's preferable. Its accuracy when compared with Zeki method is 96.81{\%} for the ACTOR printed dataset and the rate of this method is 85.81{\%} for Zeki dataset and also compared with Al Hamad method is 95.09{\%}, and 89.10{\%} for ACDARhandwrittendataset. Whereas the others datasets accuracies are 95.09{\%} for IFN-ENIT, 98.27{\%} for APTI, 91.63{\%} for AHDB, and 90.69{\%} for AHDB-FTR on same feature (EDMS) and classifier (MLP_NN). Novelty: Adapt Mathematics with segmentation process to determine whether the CC is one or more than one character. Using a mathematical model based on the VD to avoid over segmentation.",
keywords = "Arabic words, Mathematical model, More than one character, Segmentation, Voronoi diagrams",
author = "Jabril Ramdan and Khairuddin Omar and Nasrudin, {Mohammad Faidzul}",
year = "2016",
doi = "10.17485/ijst/2016/v9i40/84801",
language = "English",
volume = "9",
journal = "Indian Journal of Science and Technology",
issn = "0974-6846",
publisher = "Indian Society for Education and Environment",
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AU - Ramdan, Jabril

AU - Omar, Khairuddin

AU - Nasrudin, Mohammad Faidzul

PY - 2016

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N2 - Objectives: The objectives are to use a mathematical model to define a region-based segmentation method. This study determines whether the Connected Component (CC) is one or more than one character. Method: Whereas the other methods they tend to ignore the solid foundation of describing characters and connection points. This proposed method adopts on many stages for adaptive the mathematic in segmentation characters process are: i) peak detection from vertical histogram for (CC), and ii) enhancement of the model using a mathematical model to improve the segmentation method based on the Voronoi Diagram (VD) Through a number of peaks. Findings: Whereas characters, such as and , are confusing to segmentation methods; these errors include separating connection strokes from both sides to produce a separated one. Other errors must be handled at a later stage, such as segmenting the character at an acute angle. Whereas the mathematical model is depending on peaks, numbers, direction, and length of CC. This model is tested on segmentation using five Arabic datasets as: AHDB, IFN-ENIT, AHDB-FTR, APTI, Zeki and Al Hamad DB datasets. The Preliminary results show that the application of the EDMS feature with multi perceptron-NN classifier it's preferable. Its accuracy when compared with Zeki method is 96.81% for the ACTOR printed dataset and the rate of this method is 85.81% for Zeki dataset and also compared with Al Hamad method is 95.09%, and 89.10% for ACDARhandwrittendataset. Whereas the others datasets accuracies are 95.09% for IFN-ENIT, 98.27% for APTI, 91.63% for AHDB, and 90.69% for AHDB-FTR on same feature (EDMS) and classifier (MLP_NN). Novelty: Adapt Mathematics with segmentation process to determine whether the CC is one or more than one character. Using a mathematical model based on the VD to avoid over segmentation.

AB - Objectives: The objectives are to use a mathematical model to define a region-based segmentation method. This study determines whether the Connected Component (CC) is one or more than one character. Method: Whereas the other methods they tend to ignore the solid foundation of describing characters and connection points. This proposed method adopts on many stages for adaptive the mathematic in segmentation characters process are: i) peak detection from vertical histogram for (CC), and ii) enhancement of the model using a mathematical model to improve the segmentation method based on the Voronoi Diagram (VD) Through a number of peaks. Findings: Whereas characters, such as and , are confusing to segmentation methods; these errors include separating connection strokes from both sides to produce a separated one. Other errors must be handled at a later stage, such as segmenting the character at an acute angle. Whereas the mathematical model is depending on peaks, numbers, direction, and length of CC. This model is tested on segmentation using five Arabic datasets as: AHDB, IFN-ENIT, AHDB-FTR, APTI, Zeki and Al Hamad DB datasets. The Preliminary results show that the application of the EDMS feature with multi perceptron-NN classifier it's preferable. Its accuracy when compared with Zeki method is 96.81% for the ACTOR printed dataset and the rate of this method is 85.81% for Zeki dataset and also compared with Al Hamad method is 95.09%, and 89.10% for ACDARhandwrittendataset. Whereas the others datasets accuracies are 95.09% for IFN-ENIT, 98.27% for APTI, 91.63% for AHDB, and 90.69% for AHDB-FTR on same feature (EDMS) and classifier (MLP_NN). Novelty: Adapt Mathematics with segmentation process to determine whether the CC is one or more than one character. Using a mathematical model based on the VD to avoid over segmentation.

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