Digital Ink

Digital ink research focuses on analyzing and manipulating handwritten or digitally rendered ink for various applications, ranging from document forensics and historical manuscript analysis to improved human-computer interaction. Current research employs diverse techniques, including deep learning models for semantic segmentation and ink detection in complex scenarios (like overlapping characters or carbonized papyri), and advanced mathematical representations (e.g., Chebyshev-Sobolev series) for efficient data compression and analysis of handwritten curves. These advancements have significant implications for fields like document authentication, game design using AI, and the development of more personalized and effective writing tools.