Abstract
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.