  Dyslexia and dysgraphia are learning disabilities that significantly 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
corresponding letters. Dysgraphia, on the other side, affects writing
abilities, leading to problems with letter formation, spacing, and alignment.
Coexistence of these disorders complicates diagnosis. This necessitates a
nuanced approach that can adapt to these changes and still accurately identify
and differentiate between these disorders. This study utilizes advanced
geometrical patterns and recurrent neural networks (RNN) to identify
handwriting anomalies indicative of dyslexia and dysgraphia. Handwriting
standardized features followed by feature extraction that focuses on baseline
deviations, letter connectivity, stroke thickness and other anomalies into RNN
based autoencoder to identify irregularities. Initial results show the
challenge associated with complex pattern adaptation.
