Spatiotemporal representation of 3D hand trajectory based on beta-elliptic models

Boubaker, Houcine; Rezzoug, Nasser; Kherallah, Monji; Gorce, Philippe; Alimi, Adel M.

doi:10.1080/10255842.2014.940331

“…Also, an elliptic arc is described by the trajectory tangent on their endpoints M 1 and M 2 which is invented by [39]. Indeed, each elementary beta stroke situated between two successive velocity extrema times can be represented by…”

Section: Trajectory Modelingmentioning

confidence: 99%

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Hamdi¹,

Boubaker²,

Rabhi³

et al. 2021

Preprint

Self Cite

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Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new network architectures and combining relevant parametric models. In this paper, we propose a multi-stage deep learning-based algorithm for multilingual online handwriting recognition based on hybrid deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks. The main contributions of our work lie in partly in the composition of a new multi-stage architecture of deep learning networks associated with effective feature vectors that integrate dynamic and visual characteristics. First, the proposed system proceeds by pretreating the acquired script and delimiting its Segments of Online Handwriting Trajectories (SOHTs). Second, two types of feature vectors combining BetaElliptic Model (BEM) and Convolutional Neural Network (CNN) are extracted for each SOHT in order to fuzzy classify them into k sub-groups using DBLSTM neural networks for both online and offline branches trained using an unsupervised fuzzy k-means algorithm. Finally, we combine the trained models to strengthen the discrimination power of the global system using SVM engine. Extensive experiments on three data sets were conducted to validate the performance of the proposed method. The experimental results show the effectiveness and complementarities of the individual modules and the advantage of their fusion.<br>

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“…Also, an elliptic arc is described by the tangent of the trajectory on their endpoints M 1 and M 2 which is invented by [40]. Indeed, each elementary beta stroke located between two successive extrema speed times can be modeled by an elliptic arc characterized by four geometric parameters such as: a, b, θ, θ p as shown in Fig.3.…”

Section: Trajectory Modelingmentioning

confidence: 99%

Hybrid Architecture based on RNN-SVM for Multilingual Handwriting Recognition using Beta-elliptic and CNN models

Hamdi¹,

Boubaker²,

Rabhi³

et al. 2021

Preprint

Self Cite

2

0

View full text Add to dashboard Cite

Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new network architectures and combining relevant parametric models. In this paper, we propose a multi-stage deep learning-based algorithm for multilingual online handwriting recognition based on hybrid deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks. The main contributions of our work lie in partly in the composition of a new multi-stage architecture of deep learning networks associated with effective feature vectors that integrate dynamic and visual characteristics. First, the proposed system proceeds by pretreating the acquired script and delimiting its Segments of Online Handwriting Trajectories (SOHTs). Second, two types of feature vectors combining BetaElliptic Model (BEM) and Convolutional Neural Network (CNN) are extracted for each SOHT in order to fuzzy classify them into k sub-groups using DBLSTM neural networks for both online and offline branches trained using an unsupervised fuzzy k-means algorithm. Finally, we combine the trained models to strengthen the discrimination power of the global system using SVM engine. Extensive experiments on three data sets were conducted to validate the performance of the proposed method. The experimental results show the effectiveness and complementarities of the individual modules and the advantage of their fusion.<br>

show abstract

“…Also, an elliptic arc is described by the tangent of the trajectory on their endpoints M1 and M2 which is invented by [11]. Indeed, each elementary beta stroke located between two successive extrema speed times can be modeled by an elliptic arc characterized by four geometric parameters such as: a, b, ɵ, ɵp as shown in Fig.…”

Section: Trajectory Modelingmentioning

confidence: 99%

Hybrid Architecture based on RNN-SVM for Multilingual Handwriting Recognition using Beta-elliptic and CNN models

Hamdi¹,

Boubaker²,

Rabhi³

et al. 2021

Preprint

Self Cite

1

0

View full text Add to dashboard Cite

Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new network architectures and combining relevant parametric models. In this paper, we propose a multi-stage deep learning-based algorithm for multilingual online handwriting recognition based on hybrid deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks. The main contributions of our work lie in partly in the composition of a new multi-stage architecture of deep learning networks associated with effective feature vectors that integrate dynamic and visual characteristics. First, the proposed system proceeds by pretreating the acquired script and delimiting its Segments of Online Handwriting Trajectories (SOHTs). Second, two types of feature vectors combining BetaElliptic Model (BEM) and Convolutional Neural Network (CNN) are extracted for each SOHT in order to fuzzy classify them into k sub-groups using DBLSTM neural networks for both online and offline branches trained using an unsupervised fuzzy k-means algorithm. Finally, we combine the trained models to strengthen the discrimination power of the global system using SVM engine. Extensive experiments on three data sets were conducted to validate the performance of the proposed method. The experimental results show the effectiveness and complementarities of the individual modules and the advantage of their fusion.<br>

show abstract

Spatiotemporal representation of 3D hand trajectory based on beta-elliptic models

Cited by 15 publications

References 34 publications

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Hybrid Architecture based on RNN-SVM for Multilingual Handwriting Recognition using Beta-elliptic and CNN models

Hybrid Architecture based on RNN-SVM for Multilingual Handwriting Recognition using Beta-elliptic and CNN models

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