Individual Handwriting

Individual handwriting analysis is a burgeoning field employing machine learning to extract meaningful information from written text, encompassing both authentication and diagnostic applications. Current research focuses on developing robust algorithms, such as convolutional neural networks and latent diffusion models, to analyze various handwriting features (e.g., pressure, speed, trajectory) for tasks like writer identification, disease detection (e.g., Parkinson's, Alzheimer's, schizophrenia), and forensic document examination. These advancements offer potential for improved diagnostic tools in healthcare, enhanced security measures, and new insights into cognitive processes and neurological disorders.