scholarly journals Handwriting Quality Analysis using Online-Offline Models

2021 ◽  
Author(s):  
Yahia Hamdi ◽  
Hanen Akouaydi ◽  
Houcine Boubaker ◽  
Adel Alimi
Keyword(s):  
Writing Skills ◽  
Quality Analysis ◽  
Support Vector ◽  
Digital Devices ◽  
Children's Writing ◽  
Elliptic Model ◽  
E Learning ◽  
On Line ◽  
Interactive Interfaces ◽  
Order Position

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<br>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<br>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.<br>

scholarly journals Handwriting Quality Analysis using Online-Offline Models

2020 ◽  
Author(s):  
Yahia Hamdi ◽  
Hanen Akouaydi ◽  
Adel Alimi
Keyword(s):  
Writing Skills ◽  
Quality Analysis ◽  
Support Vector ◽  
Digital Devices ◽  
Children's Writing ◽  
Elliptic Model ◽  
E Learning ◽  
On Line ◽  
Interactive Interfaces ◽  
Order Position

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<br>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<br>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.<br>

scholarly journals Handwriting Quality Analysis using Online-Offline Models

2020 ◽  
Author(s):  
Yahia Hamdi ◽  
Hanen Akouaydi ◽  
Houcine Boubaker ◽  
Adel Alimi
Keyword(s):  
Writing Skills ◽  
Quality Analysis ◽  
Support Vector ◽  
Digital Devices ◽  
Children's Writing ◽  
Elliptic Model ◽  
E Learning ◽  
On Line ◽  
Interactive Interfaces ◽  
Order Position

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<br>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<br>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.<br>

scholarly journals Handwriting Quality Analysis using Online-Offline Models

2020 ◽  
Author(s):  
Yahia Hamdi ◽  
Hanen Akouaydi ◽  
Houcine Boubaker ◽  
Adel Alimi
Keyword(s):  
Writing Skills ◽  
Quality Analysis ◽  
Support Vector ◽  
Digital Devices ◽  
Children's Writing ◽  
Elliptic Model ◽  
E Learning ◽  
On Line ◽  
Interactive Interfaces ◽  
Order Position

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<br>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<br>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.<br>

scholarly journals Workbook for e-Education: Children’s Online Handwriting Quality Analysis.

2021 ◽  
Author(s):  
Hanen Akouaydi ◽  
Yahia Hamdi ◽  
Houcine Boubaker ◽  
Faouzi Alaya Cheikh ◽  
Adel M. Alimi
Keyword(s):  
Primary Schools ◽  
Quality Analysis ◽  
Digital Devices ◽  
Cursive Handwriting ◽  
Analysis Process ◽  
Children's Writing ◽  
Elliptic Model ◽  
E Learning ◽  
Interactive Interfaces ◽  
Order Position

<div>This paper describes an innovative e-learning project which is the development of a digital workbook that helps teaching handwriting at school. In this work, we propose a new qualitative and quantitative analysis process of cursive handwriting. This process detects automatically mistakes, gives a real-time feedback and helps teachers evaluate children’s writing skills. The main aim of this digital workbook is to help children learn how to write correctly. The proposed process is composed of five main criteria: shape,direction, stroke order, position respect to the reference lines and kinematics of the trace. It analyzes the handwriting quality and gives automatically feedback based on the Beta-Elliptic Model using similarity detection (SD) and dissimilarity distance (DD) measure. Our work apprehends dynamic and visual representation of the acquired traces and selects efficient features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbols drawing. It demonstrates that beta-elliptic is not only a model for segmentation and recognition but also a tool to evaluate handwriting. Our application offers two interactive interfaces respectively dedicated to learners, and experts or teachers who can adapt it easily to the specificity of each child. The validation of the proposed system is done on a database collected in Tunisia primary schools with 400 children. Experimental results show that the efficiency and robustness of our suggested framework that do help teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.</div>

scholarly journals Workbook for e-Education: Children’s Online Handwriting Quality Analysis.

2021 ◽  
Author(s):  
Hanen Akouaydi ◽  
Yahia Hamdi ◽  
Houcine Boubaker ◽  
Faouzi Alaya Cheikh ◽  
Adel M. Alimi
Keyword(s):  
Primary Schools ◽  
Quality Analysis ◽  
Digital Devices ◽  
Cursive Handwriting ◽  
Analysis Process ◽  
Children's Writing ◽  
Elliptic Model ◽  
E Learning ◽  
Interactive Interfaces ◽  
Order Position

<div>This paper describes an innovative e-learning project which is the development of a digital workbook that helps teaching handwriting at school. In this work, we propose a new qualitative and quantitative analysis process of cursive handwriting. This process detects automatically mistakes, gives a real-time feedback and helps teachers evaluate children’s writing skills. The main aim of this digital workbook is to help children learn how to write correctly. The proposed process is composed of five main criteria: shape,direction, stroke order, position respect to the reference lines and kinematics of the trace. It analyzes the handwriting quality and gives automatically feedback based on the Beta-Elliptic Model using similarity detection (SD) and dissimilarity distance (DD) measure. Our work apprehends dynamic and visual representation of the acquired traces and selects efficient features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbols drawing. It demonstrates that beta-elliptic is not only a model for segmentation and recognition but also a tool to evaluate handwriting. Our application offers two interactive interfaces respectively dedicated to learners, and experts or teachers who can adapt it easily to the specificity of each child. The validation of the proposed system is done on a database collected in Tunisia primary schools with 400 children. Experimental results show that the efficiency and robustness of our suggested framework that do help teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.</div>

scholarly journals Children’s Online Handwriting Quality Analysis.

2021 ◽  
Author(s):  
Hanen Akouaydi ◽  
Yahia Hamdi ◽  
Houcine Boubaker ◽  
Mourad Zaied ◽  
Faouzi Alaya Cheikh ◽  
...  
Keyword(s):  
Mobile Application ◽  
Writing Skills ◽  
Quality Analysis ◽  
Qualitative And Quantitative Analysis ◽  
Qualitative And Quantitative ◽  
Cursive Handwriting ◽  
Analysis Process ◽  
Children's Writing ◽  
E Learning ◽  
Online Handwriting

<div>An innovative e-learning project is presented in this paper, which is a mobile workbook that teaches handwriting at school. This mobile application proposes a new qualitative and quantitative analysis process of online cursive handwriting. It gives a real-time feedback, detects mistakes and helps teachers evaluate children’s writing skills. The main aim of this notebook is to aid kids learn how to write correctly. We analyze handwriting according to major criteria like shape, kinematics of the trace, position respect to the reference lines,</div><div>stroke order and direction.</div>

scholarly journals Children’s Online Handwriting Quality Analysis.

2021 ◽  
Author(s):  
Hanen Akouaydi ◽  
Yahia Hamdi ◽  
Houcine Boubaker ◽  
Mourad Zaied ◽  
Faouzi Alaya Cheikh ◽  
...  
Keyword(s):  
Mobile Application ◽  
Writing Skills ◽  
Quality Analysis ◽  
Qualitative And Quantitative Analysis ◽  
Qualitative And Quantitative ◽  
Cursive Handwriting ◽  
Analysis Process ◽  
Children's Writing ◽  
E Learning ◽  
Online Handwriting

<div>An innovative e-learning project is presented in this paper, which is a mobile workbook that teaches handwriting at school. This mobile application proposes a new qualitative and quantitative analysis process of online cursive handwriting. It gives a real-time feedback, detects mistakes and helps teachers evaluate children’s writing skills. The main aim of this notebook is to aid kids learn how to write correctly. We analyze handwriting according to major criteria like shape, kinematics of the trace, position respect to the reference lines,</div><div>stroke order and direction.</div>

Experiencia Aprendizaje colaborativo con intercambios on-line: relato de una experiencia / Collaborative learning with exchanges online: account of an experience

2015 ◽  
Vol 3 (6) ◽  
pp. 21
Author(s):  
Isabel Álvarez
Keyword(s):  
Collaborative Learning ◽  
E Learning ◽  
On Line ◽  
Aprendizaje Colaborativo ◽  
Estudiantes Universitarios

El propósito de este artículo es fortalecer la colaboración entre dos instituciones que buscan integrar e-learning en sus prácticas más cotidianas y en contextos donde antes no habían tenido experiencia previa. El objetivo principal es acercar a los estudiantes universitarios, en este caso a los usuarios del Banco del Tiempo (BdT) del Ayuntamiento de Terrassa, Barcelona, a las prácticas reales para que obtengan un aprendizaje más significativo,. La experiencia relata el proceso de coordinación, diseño, gestión y valoración desde el punto de vista del aprendizaje en la formación inicial de los estudiantes de grado.

REMOTE FORM OF EDUCATION, AS ONE OF THE METHODS OF IMPROVING MOTIVATION BY STUDENTS OF DISTANCE LEARNING

2019 ◽  
pp. 73-79
Author(s):  
I. G. Kupnovytska ◽  
V. I. Klymenko ◽  
I. P. Fitkovska ◽  
S. M. Kalugina ◽  
R. I. Belehay ◽  
...  
Keyword(s):  
Distance Learning ◽  
Faculty Members ◽  
Learning System ◽  
Teaching Materials ◽  
Internal Organs ◽  
Technology System ◽  
Course Work ◽  
E Learning ◽  
On Line ◽  
Video And Audio

The development of a modern e-learning system promotes the active introduction of distance education. The organization of a mixed form of education at the department of Clinical Pharmacology and Pharmacotherapy involves an education during the session and using the distance contact between faculty and students during the intersessional period. The information technology system of distance learning is provided by personal computers, video and audio equipment. The web-site of the department presents a set of teaching materials, including curriculas, work programs, lecture notes, test assignments, situational tasks, control and individual course work. Distance learning is supplemented with new teaching materials: web lectures, e-learning textbooks and manuals. Lectures are delivered by faculty members in the form of video conferences or webinars. Consultations are conducted in the on-line mode each week at a certain time by the teachers of the department according to the schedule. The website of the department presents methods for implementing practical skills, video stories of individual urgent states on the pathology of internal organs, demonstrates sets of medicines for seven types of first aid kits to improve the students' knowledge and skills, and to successfully pass the practical part of the state certification of graduates from the discipline "Clinical pharmacy".

Methods for detecting and correcting contextual data quality problems

2021 ◽  
Vol 25 (4) ◽  
pp. 763-787
Author(s):  
Alladoumbaye Ngueilbaye ◽  
Hongzhi Wang ◽  
Daouda Ahmat Mahamat ◽  
Ibrahim A. Elgendy ◽  
Sahalu B. Junaidu
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Web Applications ◽  
Support Vector ◽  
Similarity Metrics ◽  
Distributed Data ◽  
Contextual Data ◽  
Formal Taxonomy ◽  
E Learning ◽  
High Scalability

Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).

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