Effect of age on hand drawing movement kinematics

Dhieb, Thameur; Rezzoug, Nasser; Boubaker, Houcine; Gorce, Philippe; Alimi, Adel M.

doi:10.1080/10255842.2020.1714235

“…It consists of decomposing the online trajectory into elementary stroke based on the combination of the velocity profile (dynamic features) and static profile (geometric features) of online handwriting modeling. Further, BEM has proven highly successful in various areas of research on online handwriting like the study of handwriting recognition [25], [26], the effect of age on hand movement kinematics [27], temporal order recovery [28], [29], and writer identification [30]. To our knowledge, the BEM has not yet been used in handwriting evaluation.…”

Section: Beta-elliptic Model (Bem)mentioning

confidence: 99%

Handwriting Quality Analysis using Online-Offline Models

Hamdi¹,

Akouaydi²,

Boubaker³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

This work is part of an innovative e-learning project allowing the development of an advanced digital educational tool that provides feedback during the process of learning handwriting for young school children (three to eight years old). In this paper, we describe a new method for children handwriting quality analysis. It automatically detects mistakes, gives real-time on-line feedback for children’s writing, and helps teachers comprehend and evaluate children’s writing skills. The proposed method adjudges five main criteria: shape, direction, stroke order, position respect to the reference lines, and kinematics of the trace. It analyzes the handwriting quality and automatically gives feedback based on the combination of three extracted models: Beta-Elliptic Model (BEM) using similarity detection (SD) and dissimilarity distance (DD) measure, Fourier Descriptor Model (FDM), and perceptive Convolutional Neural Network (CNN) with Support Vector Machine (SVM) comparison engine. The originality of our work lies partly in the system architecture which apprehends complementary dynamic, geometric, and visual representation of the examined handwritten scripts and in the efficient selected features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbol drawing. The application offers two interactive interfaces respectively dedicated to learners, educators, experts or teachers and allows them to adapt it easily to the specificity of their disciples. The evaluation of our framework is enhanced by a database collected in Tunisia primary school with 400 children. Experimental results show the efficiency and robustness of our suggested framework that helps teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.

show abstract

“…It consists of decomposing the online trajectory into elementary stroke based on the combination of the velocity profile (dynamic features) and static profile (geometric features) of online handwriting modeling. Further, BEM has proven highly successful in various areas of research on online handwriting like the study of the effect of age on hand movement kinematics [23], handwriting recognition [24], temporal order recovery [25], and writer identification [26]. To our knowledge, the BEM has not yet used in handwriting evaluation.…”

Section: Beta-elliptic Model (Bem)mentioning

confidence: 99%

Handwriting Quality Analysis using Online-Offline Models

Hamdi¹,

Akouaydi²,

Boubaker³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

This work is part of an innovative e-learning project allowing the development of an advanced digital educational tool that provides feedback during the process of learning handwriting for young school children (three to eight years old). In this paper, we describe a new method for children handwriting quality analysis. It automatically detects mistakes, gives real-time on-line feedback for children’s writing, and helps teachers comprehend and evaluate children’s writing skills. The proposed method adjudges five main criteria: shape, direction, stroke order, position respect to the reference lines, and kinematics of the trace. It analyzes the handwriting quality and automatically gives feedback based on the combination of three extracted models: Beta-Elliptic Model (BEM) using similarity detection (SD) and dissimilarity distance (DD) measure, Fourier Descriptor Model (FDM), and perceptive Convolutional Neural Network (CNN) with Support Vector Machine (SVM) comparison engine. The originality of our work lies partly in the system architecture which apprehends complementary dynamic, geometric, and visual representation of the examined handwritten scripts and in the efficient selected features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbol drawing. The application offers two interactive interfaces respectively dedicated to learners, educators, experts or teachers and allows them to adapt it easily to the specificity of their disciples. The evaluation of our framework is enhanced by a database collected in Tunisia primary school with 400 children. Experimental results show the efficiency and robustness of our suggested framework that helps teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.

show abstract

“…Moreover, many other properties are reported with human handwriting movements such as speed, amplitude and script size which can be modified involuntarily without alternating the global shape of velocity profile modeling the handwriting trajectory [2]. The hybrid kinematics and geometric aspect of online handwriting generation movements are presented by the Betaelliptic model (BEM) [3] which has attained immense success in many fields such as handwriting regeneration [4] , writer identification [5] ], [6] and temporal order recovery [7] . The handwritten trajectory is normally segmented into n simple movements limited by curvature or velocity extrema called strokes.…”

Section: Introductionmentioning

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

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.

show abstract

Effect of age on hand drawing movement kinematics

Cited by 17 publications

References 2 publications

Handwriting Quality Analysis using Online-Offline Models

Handwriting Quality Analysis using Online-Offline Models

Handwriting Quality Analysis using Online-Offline Models

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

Contact Info

Product

Resources

About