Handwriting Sample

Handwriting sample analysis encompasses the study and application of handwritten text, focusing on tasks like generation, recognition, and stylistic analysis. Current research heavily utilizes deep learning models, particularly diffusion models and convolutional neural networks, often incorporating techniques like attention mechanisms and multimodal feature fusion to improve performance in applications such as writer identification, dysgraphia diagnosis, and even schizophrenia detection. These advancements are driving improvements in handwriting recognition systems and creating new tools for forensic analysis and medical diagnostics, highlighting the interdisciplinary nature and practical impact of this field. Standardized evaluation metrics are also a growing area of focus to ensure robust comparison and progress across different studies.