Multiclass Classification of Unconstrained Handwritten Arabic Words Using Machine Learning Approaches

AlKhateeb, Jawad H.; Jiang, Jianmin; Ren, Jinchang; Khelifi, Fouad; Ipson, Stanley S.

doi:10.2174/1876825300902010021

Cited by 17 publications

(5 citation statements)

References 23 publications

(37 reference statements)

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“…There are certain successful achievements on Chinese and Latin scripts but Arabic language is way behind [91,136]. According to survey, most of the works of Arabic language are based on segmentation-free approach.…”

Section: Comparison With Other Languagesmentioning

confidence: 99%

Handwritten Hindi character recognition: a review

Yadav

Purwar

Mittal³

2018

IET Image Processing

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Section: Comparison With Other Languagesmentioning

confidence: 99%

Handwritten Hindi character recognition: a review

Yadav

Purwar

Mittal³

2018

IET Image Processing

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“…For pixels clustered into a group, the average ChI-a value is also obtained for comparison. In fact, RBF kernelled SVM has been proven successful in a number of other applications [9][10][11][12], and detailed description of SVM and other machine learning approaches can be found in [1, 4, 9-10].…”

Section: The Approachmentioning

confidence: 99%

Segmentation of multispectral images and prediction of CHI-A concentration for effective ocean colour remote sensing

Ren

Zeng

McKee

2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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This version is available at https://strathprints.strath.ac.uk/54443/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. SEGMENTATION OF MULTISPECTRAL IMAGES AND PREDICTION OF CHI-A CONCENTRATION FOR EFFECTIVE OCEAN COLOUR REMOTE SENSING

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“…Various kinds of features can be found and/or calculated for an object in a PR system. Usually, features are categorized into global transformations [19], structural [20], statistical [21], and template-based matching [22].…”

Section: Background and Related Workmentioning

confidence: 99%

A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

Shayegan

Aghabozorgi

Raj

2014

Journal of Applied Mathematics

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Dimensionality reduction (feature selection) is an important step in pattern recognition systems. Although there are different conventional approaches for feature selection, such as Principal Component Analysis, Random Projection, and Linear Discriminant Analysis, selecting optimal, effective, and robust features is usually a difficult task. In this paper, a new two-stage approach for dimensionality reduction is proposed. This method is based on one-dimensional and two-dimensional spectrum diagrams of standard deviation and minimum to maximum distributions for initial feature vector elements. The proposed algorithm is validated in an OCR application, by using two big standard benchmark handwritten OCR datasets, MNIST and Hoda. In the beginning, a 133-element feature vector was selected from the most used features, proposed in the literature. Finally, the size of initial feature vector was reduced from 100% to 59.40% (79 elements) for the MNIST dataset, and to 43.61% (58 elements) for the Hoda dataset, in order. Meanwhile, the accuracies of OCR systems are enhanced 2.95% for the MNIST dataset, and 4.71% for the Hoda dataset. The achieved results show an improvement in the precision of the system in comparison to the rival approaches, Principal Component Analysis and Random Projection. The proposed technique can also be useful for generating decision rules in a pattern recognition system using rule-based classifiers.

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Multiclass Classification of Unconstrained Handwritten Arabic Words Using Machine Learning Approaches

Cited by 17 publications

References 23 publications

Handwritten Hindi character recognition: a review

Handwritten Hindi character recognition: a review

Segmentation of multispectral images and prediction of CHI-A concentration for effective ocean colour remote sensing

A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

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