Thameur Dhieb

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Parkinson’s Disease Detection from Online Handwriting based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

IEEE access, 2024

The increasing age of our society is connected to a rising number of people suffering from disord... more The increasing age of our society is connected to a rising number of people suffering from disorders. One such disorder is Parkinson's disease (PD). Predictions indicate that the number of individuals affected by PD will more, than double in the future. Neurologists and data scientists consider handwriting as one of the motor symptoms of PD and recognize it as a valuable resource for detecting this disorder. Within this framework, we introduce an innovative system for Parkinson's disease detection, which encompasses several key stages. The process commences with data augmentation and preprocessing, subsequently leading to the segmentation of online handwriting into Beta strokes. Following that, feature extraction is carried out utilizing the Beta-elliptical approach and the fuzzy perceptual detector. Finally, we employ bidirectional long short-term memory (BLSTM) for the classification task. To assess the performance of our system, we created a new online Arabic handwriting dataset designed for detecting Parkinson's disease. The results we obtained affirm the efficacy of our proposed system. Through comprehensive evaluations conducted on the PaHaW dataset, we achieved good accuracy, thereby highlighting that our system surpasses the performance of existing systems.

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Multi-head Self-attention and BGRU for Online Arabic Grapheme Text Segmentation

New In-Air Signature Datasets

A new online Arabic handwriting dataset for analyzing Parkinson’s disease

In-Air Signature Verification System based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

IEEE Access

Believing that biometrics trends are moving to distant and contactless mode, in-air signature ver... more Believing that biometrics trends are moving to distant and contactless mode, in-air signature verification is nowadays considered as one of the principal users biometric identification in contactless mode allowing users identification by drawing their handwritten signature in the air. In-air signature verification is used in many applications like access control and forensic analysis. In this regard, we propose a novel system for in-air signature verification using Beta stroke segmentation. The Beta-elliptical approach and the fuzzy perceptual detector are used for features extraction. The proposed system defines a specific data acquisition protocol and uses preprocessing steps to prepare data. Finally, the verification phase is done based on Dynamic Time Warping. To evaluate our proposed system, we have created two in-air signature datasets with and without the use of a transparent glass plate, which we make publicly available at dataport.org/documents/air-signature-databases, termed respectively In-Air Signature dataset (IAS dataset) and In-Air Signature dataset using Glass Plate (IASGP dataset). Our verification system demonstrates promising outcomes, yielding an Equal Error Rate (EER) of 1.25% when applied to the IAS dataset and an EER of 2.00% when applied to the IASGP dataset in the skilled-forgery scenario. Extensive evaluations were conducted on both the 3DAirSig and the DeepAirSig datasets. The results confirm that the proposed system has a good performance compared to existing in-air signature verification systems for both skilled-forgery and random-forgery scenarios. In-air signature verification; Beta-elliptical approach; fuzzy perceptual detector; Dynamic Time Warping.

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Deep neural network for online writer identification using Beta-elliptic model

The online writer identification is a required component in many applications of Computer vision ... more The online writer identification is a required component in many applications of Computer vision and Pattern Recognition. The offline writer identification is more developed in literature due to the use of traditional system based on Image Processing. There is a lack of works done in the case of online writer identification. In this paper, we propose a novel method to text independent writer identification from online handwriting. Our proposed method is based on the use of Beta-elliptic model that computes efficiently on real time writing movements in online handwriting by involving simultaneously its both profile entities: the Beta impulses and the elliptic arcs. The information provided by the feature extraction is used in a Deep Neural Network as classifier. The obtained results show that the proposed online writer identification method is worth to receive further exploration in capturing the writer's individual. The use of the Deep Neural Network provides more robustness to identification of writers.

Features extraction based on Extended Beta-Elliptic Model and Fuzzy Elementary Perceptual Codes

The presented code consists of the preprocessing and the segmentation of online handwriting into ... more The presented code consists of the preprocessing and the segmentation of online handwriting into a sequence of Beta strokes in a first step. Then, from each Beta stroke, we extract a set of static and dynamic features using four features extraction techniques based on the Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes. Next, all the segments which are composed of N consecutive Beta strokes are categorized into groups and subgroups according to their position and their geometric characteristics. - The first features extraction technique called Beta-Elliptic model. - The second features extraction technique called Extended Beta-Elliptic Model. - The third features extraction technique using combination between Beta-Elliptic model and Fuzzy Elementary Perceptual Codes. - The fourth features extraction technique using combination between Extended Beta-Elliptic Model and Fuzzy Elementary Perceptual Codes.

Fuzzy Perceptual Segmentation

This code is for the segmentation of online handwriting into a sequence of Beta strokes in a firs... more This code is for the segmentation of online handwriting into a sequence of Beta strokes in a first step. Then, from each Beta stroke, we extract a theta, angle of deviation of each stroke. Using this features, we classify each elliptic stoke into Elementary Perceptual Codes "EPCs" using Fuzzy Logic. After classifying into EPCs, we use LSTM for character recognition.

Features extraction based on Beta-Elliptic Model and Fuzzy Elementary Perceptual Codes

The presented code consists of the preprocessing and the segmentation of online handwriting into ... more The presented code consists of the preprocessing and the segmentation of online handwriting into a sequence of Beta strokes in a first step. Then, from each Beta stroke, we extract a set of static and dynamic features using four features extraction techniques based on the Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes. Next, all the segments which are composed of N consecutive Beta strokes are categorized into groups and subgroups according to their position and their geometric characteristics. - The first features extraction technique using Beta-Elliptic model called the Advanced Overlapped Beta Strategy (AOBS). - The second features extraction technique using Beta-Elliptic model called the Simplified Beta Strategy (SBS). - The third features extraction technique using combination between Advanced Overlapped Beta Strategy and Fuzzy Elementary Perceptual Codes (AOBSFEPC). - The fourth features extraction technique using combination between Simplified Beta Strategy and...

A novel biometric system for signature verification based on score level fusion approach

Multimedia Tools and Applications, 2022

An Online Writer Identification System based on Beta-Elliptic Model and Fuzzy Elementary Perceptual Codes

ArXiv, 2018

Actually, the ability to identify the documents authors provides more chances for using these doc... more Actually, the ability to identify the documents authors provides more chances for using these documents for various purposes. In this paper, we present a new effective biometric writer identification system from online handwriting. The system consists of the preprocessing and the segmentation of online handwriting into a sequence of Beta strokes in a first step. Then, from each Beta stroke, we extract a set of static and dynamic features using four features extraction techniques based on the Beta Elliptic model and the Fuzzy Elementary Perceptual Codes. Next, all the segments which are composed of N consecutive Beta strokes are categorized into groups and subgroups according to their position and their geometric characteristics. Finally, Deep Neural Network are used for classification. Experimental results reveal that the proposed system achieves interesting results as compared to those of the existing writer identification systems on Latin and Arabic scripts.

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

ArXiv, 2019

Due to the omnipresence of mobile devices, online handwritten scripts have become the most import... more 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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Do individual characteristics influence the beta-elliptic modeling errors during ellipse drawing movements?

Computer Methods in Biomechanics and Biomedical Engineering, 2021

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific r... more HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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ASAR 2021 Online Arabic Writer Identification Competition

Document Analysis and Recognition – ICDAR 2021 Workshops, 2021

Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes

Multimedia Tools and Applications, 2021

Deep Bidirectional Long Short-Term Memory for Online Arabic Writer Identification Based on Beta-Elliptic Model

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019

The considerable development of electronic document management system in these last years has led... more The considerable development of electronic document management system in these last years has led to a large increase in the number of documents must be controlled and archived, etc. Therefore, this is important in forensic investigations, because identifying the writer could assist in solving a crime. However, there is a lack of works done in the case of online Arabic writer identification. In this paper, we propose a novel system to text independent writer identification from online Arabic handwriting. Our proposed system is based on the use of Beta-elliptic model in feature extraction. Moreover, we explore the potential utility of Deep Bidirectional Long Short-Term Memory in classification. Experimental results admitted that the proposed system has a very good performance compared to the existing online Arabic writer identification systems.

Hybrid DBLSTM-SVM Based Beta-Elliptic-CNN Models for Online Arabic Characters Recognition

2019 International Conference on Document Analysis and Recognition (ICDAR), 2019

The deep learning-based approaches have proven highly successful in handwriting recognition which... more The deep learning-based approaches have proven highly successful in handwriting recognition which represents a challenging task that satisfies its increasingly broad application in mobile devices. Recently, several research initiatives in the area of pattern recognition studies have been introduced. The challenge is more earnest for Arabic scripts due to the inherent cursiveness of their characters, the existence of several groups of similar shape characters, large sizes of respective alphabets, etc. In this paper, we propose an online Arabic character recognition system based on hybrid Beta-Elliptic model (BEM) and convolutional neural network (CNN) feature extractor models and combining deep bidirectional long short-term memory (DBLSTM) and support vector machine (SVM) classifiers. First, we use the extracted online and offline features to make the classification and compare the performance of single classifiers. Second, we proceed by combining the two types of feature-based systems using different combination methods to enhance the global system discriminating power. We have evaluated our system using LMCA and Online-KHATT databases. The obtained recognition rate is in a maximum of 95.48% and 91.55% for the individual systems using the two databases respectively. The combination of the on-line and off-line systems allows improving the accuracy rate to 99.11% and 93.98% using the same databases which exceed the best result for other state-of-the-art systems.

Towards a novel biometric system for forensic document examination

Computers & Security, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Effect of age on hand drawing movement kinematics

Computer Methods in Biomechanics and Biomedical Engineering, 2019

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Online Arabic writer identification based on Beta-elliptic model

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), 2015

This paper proposes an automatic text-independent online Arabic writer identification system. The... more This paper proposes an automatic text-independent online Arabic writer identification system. The main contribution of our system is to explore the utility of Beta-elliptic model in features extraction for online writer identification, due to the rich output of Beta-elliptic model in terms of graphical, kinematical and biometrical data. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results on ADAB Database show the performance of the proposed system in online Arabic writer identification task.

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