Specific Learning Disabilities (SpLDs) are a common concern in today's society, manifesting in various ways and causing different difficulties in daily life. For one person, it might be a lack of attention, while for another, it could be struggling to read fluently or perform basic mathematical calculations. These challenges fall under different categories of learning disabilities. Early detection and intervention for SpLDs are crucial, as they enable timely support, leading to better outcomes for children living with these disabilities.
Early detection and intervention of SpLDs are hindered by limited expertise and limited resources for screening, diagnosis, and treatment. As a result, many children remain undiagnosed, facing stigma and labels that lead to low self-esteem and behaviour problems, further impairing their ability to learn. Our research aims to address these challenges by developing a low-cost mobile app for SpLD screening. The app will use deep learning to analyse handwriting and distinguish between individuals with and without SpLDs. The proposed solution requires only taking a photo of the handwritten text on a mobile phone, which is then processed by the prediction model to generate results.
Based on the proposed solution, the SpLDs screening can be conducted at home, in a school study area without any additional special setting. The important factors of this app are simplicity, ease of use, less training requirement, the accuracy of the results, and reliability. This app can serve from individual to national level for screening SpLDs in children. This will lead to the improvement of quality education received by ALL which in turn will contribute to wider societal improvements.