Handwriting Anomalies and Learning Disabilities through Recurrent Neural Networks and Geometric Pattern Analysis
It addresses the challenge of accurately identifying and differentiating between dyslexia and dysgraphia, which are learning disabilities affecting reading and writing, but the approach appears incremental as it builds on existing deep-learning methods for handwriting analysis.
This study tackled the problem of diagnosing dyslexia and dysgraphia by using recurrent neural networks and geometric pattern analysis on handwriting samples, achieving state-of-the-art performance on a dataset of about 33,000 samples.
Dyslexia and dysgraphia are learning disabilities that profoundly impact reading, writing, and language processing capabilities. Dyslexia primarily affects reading, manifesting as difficulties in word recognition and phonological processing, where individuals struggle to connect sounds with their corresponding letters. Dysgraphia, on the other hand, affects writing skills, resulting in difficulties with letter formation, spacing, and alignment. The coexistence of dyslexia and dysgraphia complicates diagnosis, requiring a nuanced approach capable of adapting to these complexities while accurately identifying and differentiating between the disorders. This study utilizes advanced geometrical patterns and recurrent neural networks (RNN) to identify handwriting anomalies indicative of dyslexia and dysgraphia. Handwriting is first standardized, followed by feature extraction that focuses on baseline deviations, letter connectivity, stroke thickness, and other anomalies. These features are then fed into an RNN-based autoencoder to identify irregularities. Initial results demonstrate the ability of this RNN model to achieve state-of-art performance on combined dyslexia and dysgraphia detection, while showing the challenges associated with complex pattern adaptation of deep-learning to a diverse corpus of about 33,000 writing samples.