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.
翻译:这项工作是创新电子学习项目的一部分,它有助于开发先进的数字教育工具,在幼儿(三至八岁)的学习笔迹过程中提供反馈。在本文件中,我们描述了儿童笔迹质量分析的新方法。它自动发现错误,为儿童笔迹提供实时在线反馈,帮助教师理解和评价儿童的写作技能。拟议方法判断了五个主要标准形状、方向、中风顺序、参照线的位置和追踪的动向。它分析了笔迹质量,并自动根据三种提取模型的组合提供反馈:Beta-Elliptic Model(BEM),使用类似检测(SD)和不相近距离(DDD)测量方法;它自动发现错误,为儿童提供实时在线反馈,帮助教师通过支持矢量机器(SVM)比较引擎理解儿童写字,我们工作的初衷部分在于系统结构,它能够捕捉到辅助性动态、几何和直观的手写脚本,并在教师的高效选择下,通过数字风格和图像,使学生能够分别通过数字和数字化、多脚本学习,展示他们。