Knowledge-Based Baseline Detection and Optimal Thresholding for Words Segmentation in Efficient Pre-Processing of Handwritten Arabic Text

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

doi:10.1109/itng.2008.71

Cited by 21 publications

(14 citation statements)

References 9 publications

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“…As samples of words provided in the IFN/ENIT database have been manually separated and binarized during the development stage [4], the only processes still needed are estimation of the baseline and normalization. Although not needed here, we have investigated how to generally segment words and this and our technique to estimate the baseline can be found in [21].…”

Section: Preprocessingmentioning

confidence: 99%

Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

AlKhateeb

Pauplin

Ren

et al. 2011

Knowledge-Based Systems

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This version is available at https://strathprints.strath.ac.uk/48364/ 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. Experimental results and quantitative evaluations showed that HMM outperforms DBN in terms of higher recognition rate and lower complexity. Performance of Hidden Markov Model and Dynamic Bayesian Network Classifiers on Handwritten Arabic Word Recognition

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Section: Preprocessingmentioning

confidence: 99%

Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

AlKhateeb

Pauplin

Ren

et al. 2011

Knowledge-Based Systems

Self Cite

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“…Since in our test sets of IFN/ENIT database all words have been cropped out during the development stage [5], the only thing we need to do as preprocessing is baseline detection and normalization. Regarding technical details on word segmentation as well as base line detection, please refer to our previous work in [24].…”

Section: Preprocessingmentioning

confidence: 99%

Multiclass Classification of Unconstrained Handwritten Arabic Words Using Machine Learning Approaches

AlKhateeb¹,

Jiang²,

Ren³

et al. 2009

TOSIGPJ

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Abstract:In this paper, we propose and describe efficient multiclass classification and recognition of unconstrained handwritten Arabic words using machine learning approaches which include the K-nearest neighbor (K-NN) clustering, and the neural network (NN). The technical details are presented in terms of three stages, namely preprocessing, feature extraction and classification. Firstly, words are segmented from input scripts and also normalized in size. Secondly, from each of the segmented words various feature extraction methods are introduced. Finally, these features are utilized to train the K-NN and the NN classifiers for classification. In order to validate the proposed techniques, extensive experiments are conducted using the K-NN and the NN. The proposed algorithms are tested on the IFN/ENIT database which contains 32492 Arabic words; the proposed algorithms give good accuracy when compared with other methods.

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“…Therefore, the only things researchers need to do are estimation of the baseline and normalization. Despite the availability of cropped words in the database, we have investigated how to segment words and estimate the baseline and details can be found in [10].…”

Section: A Preprocessingmentioning

confidence: 99%

A Machine Learning Approach for Classifying Offline Handwritten Arabic Words

AlKhateeb

Ren

Jiang

et al. 2009

2009 International Conference on CyberWorlds

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In this paper, a machine learning approach for classifying handwritten Arabic word is proposed, which includes three stages including preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Secondly, three different feature extraction methods are applied to each segmented word namely the discrete cosine transform (DCT), moment invariants, and absolute mean value of overlapping blocks. Finally, theses features are utilized to train a neural network for classification. This approach has been tested using the IFN/ENIT database which consists of 32492 Arabic words. The proposed approach gives a good accuracy when compared with other methods.

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Knowledge-Based Baseline Detection and Optimal Thresholding for Words Segmentation in Efficient Pre-Processing of Handwritten Arabic Text

Cited by 21 publications

References 9 publications

Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

Multiclass Classification of Unconstrained Handwritten Arabic Words Using Machine Learning Approaches

A Machine Learning Approach for Classifying Offline Handwritten Arabic Words

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