Off-Line Arabic Character Recognition – A Review

Khorsheed, M. S.

doi:10.1007/s100440200004

Cited by 171 publications

(117 citation statements)

References 72 publications

Supporting

Mentioning

117

Contrasting

Order By: Relevance

“…Segmentation/Skeletonization:-Skeleton-based features work on the word and characters of the skeleton image which is obtained by thinning the binary raster representation of the word image or character image [83]. GSC features were first proposed by [82] and were designed to work with the binarized images.…”

Section: Pre-processing:-mentioning

confidence: 99%

Character Recognition Based on Skeletonization: A Survey.

Sultane¹,

Bhalerao²,

Bonde.³

2017

IJAR

View full text Add to dashboard Cite

Section: Pre-processing:-mentioning

confidence: 99%

Character Recognition Based on Skeletonization: A Survey.

Sultane¹,

Bhalerao²,

Bonde.³

2017

IJAR

View full text Add to dashboard Cite

“…The HMMs are statistical models which originally used for speech recognition effectively. Due to the success of the HMMs in speech recognition and due to the similarities between the recognition of speech and cursive handwriting, HMMs were extended for online and off-line handwriting recognition [3,28].…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

“…The latter, which can be regarded as a global approach, takes the whole word image for recognition and therefore needs no segmentation. Although the global approach makes the recognition process simpler, it requires a larger input vocabulary than analytical approach [3]. This paper focuses on the Arabic handwritten word recognition phase and introduces new methods for extracting features.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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

show abstract

“…Exhaustive work has been contributed on printed text and relatively very less amount of research has been reported on handwritten text [1,2,3,4,5,6,7,8,9,10]. A comprehensive survey on handwritten character recognition were reported in [11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Multi-stage Strategy to Classify Handwritten Characters of Telugu

2017

IJCRR

View full text Add to dashboard Cite

The aim of this work is to recognize handwritten characters of Indian language, Telugu. Single stage of classifying similar Telugu characters leads to low recognition rate. However similar characters of Telugu (Indian language) are recognized in two stages in the current work. Various preprocessing steps are carried out first to extract characters from the handwritten documents. The preprocessed characters are then utilized to extract features from them. These features are further used in the proposed two-stage classification. The misclassified characters from the first stage of classification are fed to the second classifier in the proposed method. The recognition rates obtained with the two stage system are better compared to the single stage classification system.

show abstract

Off-Line Arabic Character Recognition – A Review

Cited by 171 publications

References 72 publications

Character Recognition Based on Skeletonization: A Survey.

Character Recognition Based on Skeletonization: A Survey.

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

Multi-stage Strategy to Classify Handwritten Characters of Telugu

Contact Info

Product

Resources

About