Machine recognition and correction of printed Arabic text

Amin, Adnan; Mari, Jean‐François

doi:10.1109/21.44052

Cited by 88 publications

(28 citation statements)

References 7 publications

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“…The application of HMMs to Arabic OCR was first attempted by Amin and Mari [1]. Subsequently Khorsheed and Clocksin [2] present a technique for the offline recognition of cursive Arabic script based on an HMM.…”

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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Section: Related Studiesmentioning

confidence: 99%

Holistic Approach for Arabic Word Recognition

Wahbi¹,

Musa²

2016

IJCATR

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“…With this, bounding rectangular boxes could then be drawn around the baselines. The algorithm adopted in drawing these bounding boxes was similar to that of [40,41].…”

Section: Preprocessingmentioning

confidence: 99%

Pixel-Based Unsupervised Classification Approach for Information Detection on Optical Markup Recognition Sheet

Gyamfi¹,

Missah²

2017

Adv. sci. technol. eng. syst. j.

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“…In 2002, Hamami and Berkani developed a structural approach to handle many fonts and it included rules to prevent over-segmentation [10].…”

Section: Related Workmentioning

confidence: 99%

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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Machine recognition and correction of printed Arabic text

Cited by 88 publications

References 7 publications

Holistic Approach for Arabic Word Recognition

Holistic Approach for Arabic Word Recognition

Pixel-Based Unsupervised Classification Approach for Information Detection on Optical Markup Recognition Sheet

A Recognition-Based Approach to Segmenting Arabic Handwritten Text

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