Structural Features of Cursive Arabic Script

Khorsheed, M. S.; Clocksin, William F.

doi:10.5244/c.13.42

Cited by 40 publications

(31 citation statements)

References 4 publications

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“…Adjacent letters that form shapes similar to those of classes 15,16,17,18,19,20,21 and 22 may wrongly be combined although they are correctly classified before grouping. This scenario is very clear in Figure 9, in the word ‫,"ﺳﯿﺪي"‬ where the adjacent letters ‫"ﯿ"‬ and part of the letter ‫"ﺳ"‬ were recognized and classified as letter ‫,"ﺳ"‬ and the remaining part of ‫"ﺳ"‬ was classified as letter ‫.…”

Section: Resultsmentioning

confidence: 99%

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A Recognition-Based Approach to Segmenting Arabic Handwritten Text

Elnagar¹,

Bentrcia²

2015

JILSA

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Segmenting Arabic handwritings had been one of the subjects of research in the field of Arabic character recognition for more than 25 years. The majority of reported segmentation techniques share a critical shortcoming, which is over-segmentation. The aim of segmentation is to produce the letters (segments) of a handwritten word. When a resulting letter (segment) is made of more than one piece (stroke) instead of one, this is called over-segmentation. Our objective is to overcome this problem by using an Artificial Neural Networks (ANN) to verify the resulting segment. We propose a set of heuristic-based rules to assemble strokes in order to report the precise segmented letters. Preprocessing phases that include normalization and feature extraction are required as a prerequisite step for the ANN system for recognition and verification. In our previous work [1], we did achieve a segmentation success rate of 86% but without recognition. In this work, our experimental results confirmed a segmentation success rate of no less than 95%.

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Section: Resultsmentioning

confidence: 99%

“…Another method was presented by Pechwitz and Maergner [18], where the recognition system was based on a semi-continuous 1-dimensional HMM. From each input word, features were collected using sliding window approach.…”

Section: Related Workmentioning

confidence: 99%

A Recognition-Based Approach to Segmenting Arabic Handwritten Text

Elnagar¹,

Bentrcia²

2015

JILSA

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“…We choose males names from SUST dataset with 100 samples per class for training and 50 samples per class for testing, figure (2) show all processes in details as follows.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Subsequently Khorsheed and Clocksin [2] present a technique for the offline recognition of cursive Arabic script based on an HMM. AlKhateeb et al design a word-based off-line recognition system using Hidden Markov Models (HMMs).…”

Section: Related Studiesmentioning

confidence: 99%

Holistic Approach for Arabic Word Recognition

Wahbi¹,

Musa²

2016

IJCATR

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Abstract. Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large training dataset, researchers currently study Holistic Approach "recognition of a word without segmentation". This paper describes a method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also show that best number of state for each word need careful selection and considerations.

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“…Khorsheed and Clocksin [5] presented a technique for the word can be recognized as single unit which depends on a predefined lexicon. Using the skeleton of the word based on the Stentiford's algorithm [6], all segments were extracted for recognition into feature vector.…”

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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Structural Features of Cursive Arabic Script

Cited by 40 publications

References 4 publications

A Recognition-Based Approach to Segmenting Arabic Handwritten Text

A Recognition-Based Approach to Segmenting Arabic Handwritten Text

Holistic Approach for Arabic Word Recognition

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

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