Off-Line Features Integration for On-Line Handwriting Graphemes Modeling Improvement

Boubaker, Houcine; Chaabouni, Aymen; Kherallah, Monji; Alimi, Adel M.; Abed, Haikal El

doi:10.1109/icfhr.2012.244

Cited by 4 publications

(6 citation statements)

References 17 publications

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“…Formula 6 offers a means to compute the velocity profile. (6) In their research paper [21], Boubaker et al present an innovative method for modeling the velocity profile. They introduce the simplified Beta-Elliptic model, which includes a continuous training component.…”

Section: Features Extractionmentioning

confidence: 99%

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

Allebawi,

Dhieb,

Neji

et al. 2024

IEEE Access

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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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Section: Features Extractionmentioning

confidence: 99%

“…• The Beta-elliptical approach [20], [21] and the fuzzy perceptual detector [22], [23] are adopted for the first time in the features extraction to the PD detection system.…”

Section: Introductionmentioning

confidence: 99%

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

Allebawi,

Dhieb,

Neji

et al. 2024

IEEE Access

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“…Firstly, to get the correct path of the signature in the air even if the viewpoint changes during the signing, we will incorporate the MediaPipe Hands framework [12] into our work, offering accurate finger and hand tracking capabilities. Secondly, in order to well characterize the in-air signature, we will use the Betaelliptical approach [13], [14] and the fuzzy perceptual detector [15], [16]. Finally, to tackle the issue of having a limited number of reference signatures for each signer, we will utilize the Dynamic Time Warping algorithm (DTW).…”

Section: B Contributionsmentioning

confidence: 99%

“…we are interested to use Beta-elliptical approach in the in-air signature verification system, which sufficiently represents the hand movements in real-time, by describing together its profile parts: the Beta impulses and the elliptic arcs [13], [14], [30]. Besides, we are interested to utilize the fuzzy perceptual detector in order to adequately represent the hand movements path [15], [16].…”

Section: Features Extractionmentioning

confidence: 99%

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

Alobaidi,

Dhieb,

Hamdani

et al. 2023

IEEE Access

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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 https://ieeedataport.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. INDEX TERMSIn-air signature verification; Beta-elliptical approach; fuzzy perceptual detector; Dynamic Time Warping.

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Feature Extractor Based Deep Method to Enhance Online Arabic Handwritten Recognition System

Elleuch

Zouari

Kherallah

2016

Artificial Neural Networks and Machine Learning – ICANN 2016

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Off-Line Features Integration for On-Line Handwriting Graphemes Modeling Improvement

Cited by 4 publications

References 17 publications

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

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

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

Feature Extractor Based Deep Method to Enhance Online Arabic Handwritten Recognition System

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