A statistical global feature extraction method for optical font recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

The study of optical font recognition has becoming more popular nowadays. In line to that, global analysis approach is extensively used to identify various font type to classify writer identity. Objective of this paper is to propose an enhanced global analysis method. Based on statistical analysis of edge pixels relationships, a novel method in feature extraction for binary images has proposed. We test the proposed method on Arabic calligraphy script image for optical font recognition application. We classify those images using Multilayer Network, Bayes network and Decision Tree classifiers to identify the Arabic calligraphy type. The experiments results shows that our proposed method has boost up the overall performance of the optical font recognition.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages257-267
Number of pages11
Volume6591 LNAI
EditionPART 1
DOIs
Publication statusPublished - 2011
Event3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011 - Daegu
Duration: 20 Apr 201122 Apr 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6591 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011
CityDaegu
Period20/4/1122/4/11

Fingerprint

Binary images
Decision trees
Feature Extraction
Feature extraction
Statistical methods
Multilayers
Classifiers
Pixels
Global Analysis
Experiments
Classify
Binary Image
Bayes
Decision tree
Statistical Analysis
Multilayer
Pixel
Classifier
Line
Experiment

Keywords

  • Arabic calligraphy script
  • Font recognition
  • Global feature extraction
  • Gray level co-occurrence matrix
  • Statistical feature extraction

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Bataineh, B., Sheikh Abdullah, S. N. H., & Omar, K. (2011). A statistical global feature extraction method for optical font recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 6591 LNAI, pp. 257-267). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6591 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-20039-7-26

A statistical global feature extraction method for optical font recognition. / Bataineh, Bilal; Sheikh Abdullah, Siti Norul Huda; Omar, Khairuddin.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6591 LNAI PART 1. ed. 2011. p. 257-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6591 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bataineh, B, Sheikh Abdullah, SNH & Omar, K 2011, A statistical global feature extraction method for optical font recognition. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 6591 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6591 LNAI, pp. 257-267, 3rd International Conference on Intelligent Information and Database Systems, ACIIDS 2011, Daegu, 20/4/11. https://doi.org/10.1007/978-3-642-20039-7-26
Bataineh B, Sheikh Abdullah SNH, Omar K. A statistical global feature extraction method for optical font recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 6591 LNAI. 2011. p. 257-267. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-20039-7-26
Bataineh, Bilal ; Sheikh Abdullah, Siti Norul Huda ; Omar, Khairuddin. / A statistical global feature extraction method for optical font recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6591 LNAI PART 1. ed. 2011. pp. 257-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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