Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition

Benouareth, Abdallah; Ennaji, Abdellatif; Sellami, Mokhtar

doi:10.1016/j.patrec.2008.05.008

Cited by 57 publications

(28 citation statements)

References 35 publications

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“…It is 90.26% for top1, 94.71 for top2, and 95.68% for top3. It is important to mention that this system used sets a, b, and c for training and set d for testing Benouareth et al [17,18] presented an off-line unconstrained handwritten Arabic word recognition based on semi-continuous hidden Markov models (SCHMMs) with explicit state duration. Statistical and structural features were utilized on the basis of the adopted segmentation in which implicit word segmentation is used to divide images into vertical frames of constant and variable width for feature extraction.…”

Section: Literature Reviewmentioning

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: Literature Reviewmentioning

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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“…HMMs with explicit state duration are defined by the parameters: A, B, N, p(d) and π that are respectively state transition probability matrix, output pdf vector, a total number of HMM states, a state duration probability vector and initial state probability vector. Steps required for state duration modeling in generative HMM (Benouareth et al 2008) are given in Table 1. The probability of generating d observations, i.e., P (O|λ) of an HMM λ can be computed by a generalized forwardbackward algorithm (Levinson 1986) as follows:…”

Section: Variable Duration Modeling Proceduresmentioning

confidence: 99%

Acoustic modeling problem for automatic speech recognition system: advances and refinements (Part II)

Aggarwal

Dave

2011

Int J Speech Technol

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In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have been widely used for modeling the temporal speech signal. As discussed in Part I, the conventional acoustic models used for ASR have many drawbacks like weak duration modeling and poor discrimination. This paper (Part II) presents a review on the techniques which have been proposed in literature for the refinements of standard HMM methods to cope with their limitations. Current advancements related to this topic are also outlined. The approaches emphasized in this part of review are connectionist approach, explicit duration modeling, discriminative training and margin based estimation methods. Further, various challenges and performance issues such as environmental variability, tied mixture modeling, and handling of distant speech signals are analyzed along with the directions for future research.

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“…In Benouareth et al [22,23] statistical and structural features were utilized on the basis of the adopted segmentation in which implicit word segmentation is used to divide images into vertical frames of constant width for feature extraction. Based on maxima and minima analysis of the vertical projection histogram, morphological complexity of the Arabic handwritten characters is further considered.…”

Section: State Of the Artmentioning

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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Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition

Cited by 57 publications

References 35 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

Acoustic modeling problem for automatic speech recognition system: advances and refinements (Part II)

Multiclass Classification of Unconstrained Handwritten Arabic Words Using Machine Learning Approaches

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