Three Evaluation Criteria's towards a Comparison of Two Characters Segmentation Methods for Handwritten Arabic Script

Samoud, Fadoua Bouafif; Maddouri, Samia Snoussi; Amiri, Hamid

doi:10.1109/icfhr.2012.283

Cited by 5 publications

(8 citation statements)

References 3 publications

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“…Submitted: 2018-06-18 ISSN: 2297-623X, Vol. 14, pp [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Accepted: 2018-08-10 doi:10.18052/www.scipress.com/IJET. 14…”

Section: International Journal Of Engineering and Technologiesunclassified

“…The proposed approach is based on multi-agent systems to detect and combine the components connected belonging to the same line. Samoud et al [20] propose three criteria of evaluation for the comparison of two methods of segmentation for Arabic handwritten words. The first method of segmentation is based on a combination of projection and the minima and maxima of the out-line of the picture.…”

Section: Related Workmentioning

confidence: 99%

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New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method

Fergani

Bennia

2018

IJET

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A new hybrid system of off-line analytical recognition of Arabic handwriting combining a neural network type multi-layer perceptron (MLP) and hidden Markov models (HMM) is presented. We propose a way to cooperate HMM and MLP neural network in a probabilistic architecture taking advantage of both tools dedicated to the recognition of Arabic literal amounts. This description is based on statistical and structural characteristics extraction of the significant character of the handwritten Arabic words, which can be used in the MLP classification module to estimate probabilities used as the observations to perform a recognition by the HMM. The originality of our approach is based on the segmentation into characters taking into account diacritics with the characters that match them. The experiments show the convergence of the global system, even with a random initialization of the neural network.Keywords - Recognition of Arabic handwriting, hidden Markov models, fast K-means, Arabic literal amounts, multi-layer perceptron. * E-mail: khaoula_1190@hotmail.com

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“…Submitted: 2018-06-18 ISSN: 2297-623X, Vol. 14, pp [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Accepted: 2018-08-10 doi:10.18052/www.scipress.com/IJET. 14…”

Section: International Journal Of Engineering and Technologiesunclassified

Section: Related Workmentioning

confidence: 99%

New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method

Fergani

Bennia

2018

IJET

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“…The one method was on the basis of the analysis of the contour minima and maxima and the projection. The two methods were on the basis of Hough Transform and also Mathematical Morphology operators [14]. Al Hamad.H in 2013 proposed fusion equations for improving the segmentation of Arabic word, this method has two phases.…”

Section: Previous Workmentioning

confidence: 99%

“…Therefor the dot points (DP) is the union of the set of all isolated pixels, and the set of pixels that belong to CCs that are less in size than an adaptive threshold T proportional to the estimated character size calculated upon the thinned text image. DP = {P|N8(P) = 0}∪{P|P ∈ CC ∧ size(CC) < T} (14) (iv) End Points (EP) The endpoint is the beginning or end of a word segment. The end point in the skeleton with only one neighbor, which also marks the completion of the strokes, the endpoint is determined by examining each individual one pixel in the bitmap skeletons.…”

Section: The Main Feature Pointsmentioning

confidence: 99%

Segmentation Accuracy for Offline Arabic Handwritten Recognition Based on Bounding Box Algorithm

Ismail¹

2016

CAE

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Character segmentation plays an important role in the Arabic optical character recognition (OCR) system, because the letters incorrectly segmented perform to unrecognized character. Accuracy of character recognition depends mainly on the segmentation algorithm used. The domain of off-line handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different segmentation algorithms for off-line Arabic handwriting recognition have been proposed and applied to various types of word images. This paper provides modify segmentation algorithm based on bounding box to improve segmentation accuracy using two main stages: preprocessing stage and segmentation stage. In preprocessing stage, used a set of methods such as noise removal, binarization, skew correction, thinning and slant correction, which retains shape of the character. In segmentation stage, the modify bounding box algorithm is done. In this algorithm a distance analysis use on bounding boxes of two connected components (CCs): main (CCs), auxiliary (CCs). The modified algorithm is presented and taking place according to three cases. Cut points also determined using structural features for segmentation character. The modified bounding box algorithm has been successfully tested on 450 word images of Arabic handwritten words. The results were very promising, indicating the efficiency of the suggested approach.

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“…The holes lead to a disconnect in the word body. Most Arabic handwriting segmentation errors are caused by gaps or discontinuity [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

AbdAllah¹,

Viriri²

2020

SIPIJ

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The main aim of this study is the assessment and discussion of a model for hand-written Arabic through segmentation. The framework is proposed based on three steps: pre-processing, segmentation, and evaluation. In the pre-processing step, morphological operators are applied for Connecting Gaps (CGs) in written words. Gaps happen when pen lifting-off during writing, scanning documents, or while converting images to binary type. In the segmentation step, first removed the small diacritics then bounded a connected component to segment offline words. Huge data was utilized in the proposed model for applying a variety of handwriting styles so that to be more compatible with real-life applications. Consequently, on the automatic evaluation stage, selected randomly 1,131 images from the IESK-ArDB database, and then segmented into sub-words. After small gaps been connected, the model performance evaluation had been reached 88% against the standard ground truth of the database. The proposed model achieved the highest accuracy when compared with the related works.

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Three Evaluation Criteria's towards a Comparison of Two Characters Segmentation Methods for Handwritten Arabic Script

Cited by 5 publications

References 3 publications

New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method

New Segmentation Method for Analytical Recognition of Arabic Handwriting Using a Neural-Markovian Method

Segmentation Accuracy for Offline Arabic Handwritten Recognition Based on Bounding Box Algorithm

Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

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