Arabic handwriting recognition using baseline dependant features and hidden Markov modeling

El-Hajj, Ramy; Likforman-Sulem, Laurence; Mokbel, Chafic

doi:10.1109/icdar.2005.53

Cited by 125 publications

(99 citation statements)

References 5 publications

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“…Again, this is due to the fact that the probabilistic state transfer in HMM has the intrinsic capacity in modelling connected nature of Arabic cursive script [12,14,24]. On the other hand, it is surprising to find that DBN performs much worse than HMM, although HMM is regarded as a much simplified version of DBN [19].…”

Section: Comparing With Other Systemsmentioning

confidence: 81%

“…They used the sliding approach for extracting the features [14]. Their system relies on combining three homogeneous HMM classifiers in order to increase the system performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In El-Hajj et al [14], a similar sliding window is used in such a window, the word image is divided into vertical overlapping frames with a constant width and variable heights. They validated their system using the IFN/ENIT benchmark database [4].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first stage system was based on three HMM based classifiers which used pixel features in [14]. Each HMM classifier produces the best ten candidates (Top 10) based on the likelihood.…”

Section: Literature Reviewmentioning

confidence: 99%

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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: Comparing With Other Systemsmentioning

confidence: 81%

“…They used the sliding approach for extracting the features [14]. Their system relies on combining three homogeneous HMM classifiers in order to increase the system performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…The first stage system was based on three HMM based classifiers which used pixel features in [14]. Each HMM classifier produces the best ten candidates (Top 10) based on the likelihood.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…This feature extraction approach [1] uses a sliding window to calculate pixel distribution and concavities of a word image. The extracted features are classified into the following types: distribution features based on foreground (black) pixel densities, and concavity features.…”

Section: Pixel Distribution and Concavitiesmentioning

confidence: 99%

How to Improve a Handwriting Recognition System

Abed

Märgner

2009

2009 10th International Conference on Document Analysis and Recognition

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The recognition of handwritten characters, words, and text arouses great interest today. To develop the best working system is subject of many papers published. With this paper, methods to improve the performance of existing word recognition systems are discussed. The availability of a sufficient data sets for training and testing the system assumed, optimization algorithms are presented. The usage of different feature sets and the combination of different recognizers are proposed. Tests with Arabic handwriting recognition systems using the reference IfN/ENIT-database show the usefulness of the proposed methods. An improvement of the recognition rate of up to 28% of the best single system is achieved.

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Middle Eastern Character Recognition

Belaïd¹,

Razzak²

2014

Handbook of Document Image Processing and Recognition

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Arabic handwriting recognition using baseline dependant features and hidden Markov modeling

Cited by 125 publications

References 5 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

How to Improve a Handwriting Recognition System

Middle Eastern Character Recognition

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