Online Arabic writer identification based on Beta-elliptic model

Dhieb, Thameur; Ouarda, Wael; Boubaker, Houcine; Halima, Mohamed Ben; Alimi, Adel M.

doi:10.1109/isda.2015.7489203

Cited by 21 publications

(3 citation statements)

References 29 publications

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“…The Beta elliptic was performed in systems for writer identification and verification. [29], [30], [31] An online handwriting script can be segmented into segments and each segment is a group of strokes. In fact, the number of strokes is identified automatically from the curvilinear velocity representation.…”

Section: Segmentation Stepmentioning

confidence: 99%

Neural Architecture based on Fuzzy Perceptual Representation For Online Multilingual Handwriting Recognition

Akouaydi,

Njah,

Ouarda

et al. 2019

Preprint

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Due to the omnipresence of mobile devices, online handwritten scripts have become the most important feeding input to smartphones and tablet devices. To increase online handwriting recognition performance, deeper neural networks have extensively been used. In this context, our paper handles the problem of online handwritten script recognition based on extraction features system and deep approach system for sequences classification. Many solutions have appeared in order to facilitate the recognition of handwriting. Accordingly, we used an existent method and combined with new classifiers in order to get a flexible system. Good results are achieved compared to online characters and words recognition system on Latin and Arabic scripts. The performance of our two proposed systems is assessed by using five databases. Indeed, the recognition rate exceeds 98%.

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Section: Segmentation Stepmentioning

confidence: 99%

Neural Architecture based on Fuzzy Perceptual Representation For Online Multilingual Handwriting Recognition

Akouaydi,

Njah,

Ouarda

et al. 2019

Preprint

Self Cite

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“…We often forecast weather [1][2][3] [4], wind speed [5] [6], word speech recognition [7], image recognition [8][9] [10][11] [12], face recognition [13], writer identi cation [14], stock market [15], electricity [16][17] [18][19] [20], etc. Meanwhile, forecasting literature contains a wide diversity of techniques that can be organized into two main families [20] [21]:…”

Section: Introductionmentioning

confidence: 99%

Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method

Krichene¹,

Ouarda²,

Chabchoub³

et al. 2021

Preprint

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A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets.

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“…In work [9], the use of subtractive clustering is suggested to capture the various writing styles/prototypes of handwriting. The authors in [10, 11] employ the utility of Beta‐elliptic model, to describe the spatial and velocity profiles of the segmented sub‐stroke. In another work, the writing styles present in the pre‐segmented graphemes are represented by using the multi‐fractal concept [12].…”

Section: Introductionmentioning

confidence: 99%

Online writer identification system using adaptive sparse representation framework

Venugopal

Sundaram

2020

IET biom.

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This study explores an adaptive sparse representation approach for online writer identification. The main focus is on employing prior information that quantifies the degree of importance of a dictionary atom concerning a given writer. This information is proposed by a fusion of two derived components. The first component is a saliency measure obtained from the sum-pooled sparse coefficients corresponding to the sub-strokes of a set of enrolled writers. The second component is a similarity score, computed for each dictionary atom with regards to a given writer, that is related to the reconstruction error of the sub-stroke based feature vectors. The proposed identification is accomplished with an ensemble of support vector machines (SVMs), wherein the input to the SVM trained for a writer is obtained by incorporating the adapted saliency values of that writer on the document descriptor obtained via average pooling of sparse codes. Experiments performed on the IAM and IBM-UB1 online handwriting databases demonstrate the efficacy of the proposed scheme.

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Online Arabic writer identification based on Beta-elliptic model

Cited by 21 publications

References 29 publications

Neural Architecture based on Fuzzy Perceptual Representation For Online Multilingual Handwriting Recognition

Neural Architecture based on Fuzzy Perceptual Representation For Online Multilingual Handwriting Recognition

Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method

Online writer identification system using adaptive sparse representation framework

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