HMM and fuzzy logic: A hybrid approach for online Urdu script-based languages’ character recognition

Razzak, Muhammad Imran; Anwar, Fareeha; Husain, SA; Belaïd, Abdel; Sher, Muhammad

doi:10.1016/j.knosys.2010.06.007

Cited by 48 publications

(18 citation statements)

References 24 publications

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“…Regarding the speed, HMMs were faster in training and testing time. For future investigation, we may consider introducing fuzzy logic in classification [38], exploring the possibility for more efficiency [39] and adding feature selection [40] in our system. …”

Section: Discussionmentioning

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: Discussionmentioning

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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“…On the basis of target-set, Urdu handwriting recognition (both off-line and online) can be placed into three categories: isolated or full form character recognition [20,21,22,23], selecting ligatures for recognition or holistic approach (also known as segmentation-free approach) [17,24,25,26,27,28], and segmentation-based or analytical approach [29,30,31,32,33,34,35].…”

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confidence: 99%

“…With trained non-native writer's scribbled data, the recognition rate barely increased to 73%. Razzak et al [27,28] investigated the recognition system for 1800 ligatures. By utilizing the features based on fuzzy rules and hidden Morkov model, they secured 87.6% recognition rate for Urdu Nastalique font and 74.1% for Naskh font.…”

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confidence: 99%

“…That is why, researchers in their works have tried to recognize selective number of ligatures through which many words can be composed, but not all. Consequently, such systems have limited vocabulary available for processing [27,38,19]. Furthermore, for acquiring a valid ligature or finding an optimum word, dictionary based search becomes a necessary part of the work [26], however, this is not the case with the half forms.…”

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confidence: 99%

“…Based on the number of strokes in a character and the position and shape of diacrtics, segregation of larger character set into smaller subsets is obtained through the proposed pre-classification in contrast to the previous online Urdu character recognition approaches like [20,25,26,21,27,28,38]. 3.…”

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confidence: 99%

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A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

2018

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Online handwritten Urdu character recognition is one of the key technologies for intelligent interface on smart phones and touch screens. It is a challenging research topic as Urdu script has many similar character groups. A novel similar character discrimination method for online handwritten Urdu character recognition is proposed in this paper which includes pre-classification, feature extraction and fine classification process. The pre-classifier enables the discrimination of similar characters by putting them in distinct smaller subsets according to stroke number and diacritics. Then structural features and wavelet features are extracted. Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Recurrent Neural Network (RNN) classifiers are compared for fine classification within subsets. Results of RNN classifier without using the proposed pre-classifier and features have also been obtained to check the end-to-end capability of the RNN classifier. Experimental results show that the proposed method is efficient and achieves an overall accuracy of 96% on a large-scale self-collected dataset. It is feasible to extend this method for other Arabic scripts.

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Middle Eastern Character Recognition

Belaïd¹,

Razzak²

2014

Handbook of Document Image Processing and Recognition

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HMM and fuzzy logic: A hybrid approach for online Urdu script-based languages’ character recognition

Cited by 48 publications

References 24 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

A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

Middle Eastern Character Recognition

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