Deep Learning-Based Recognition System for Hand-Drawn Sketches by Children with Down Syndrome

Children with Down Syndrome (DS) often face challenges in traditional learning environments due to difficulties in encoding information and expressing their thoughts through conventional means. To address these challenges, this research presents the development and evaluation of a deep learning-based recognition system specifically designed for interpreting hand-drawn sketches by children with DS. The system aims to facilitate more effective communication and learning by accurately recognizing and analyzing these sketches, providing valuable insights into the cognitive and creative capabilities of the children. The proposed system utilizes convolutional neural networks (CNNs) to classify and interpret sketches drawn by DS children, comparing them with standard reference samples provided by educators. Our study focuses on the application of this system within an E-Learning environment, where it serves as a tool to enhance the educational experience by offering tailored feedback and support to each child based on their unique inputs. The system was tested on a dataset of hand-drawn images collected from local DS children, and the results demonstrated high accuracy in recognition and classification. Additionally, the system's performance was evaluated against traditional methods, showing significant improvements in both accuracy and the ability to adapt to the diverse drawing styles of the children. This research underscores the potential of deep learning technologies in creating more inclusive and effective educational tools for children with special needs, paving the way for further innovations in this field.