Handwritten Trajectory

Handwritten trajectory analysis focuses on understanding and modeling the movement patterns captured in handwritten text and drawings, aiming to improve recognition accuracy and extract meaningful information from these data. Current research emphasizes the use of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), along with large language models (LLMs) to analyze both the spatial and temporal aspects of trajectories. These advancements have implications for various applications, such as autonomous driving, human mobility studies, and sign language recognition, by enabling more accurate prediction, classification, and generation of trajectories. Furthermore, research addresses challenges related to data privacy, trajectory recovery from incomplete or aggregated data, and the efficient extraction of semantic information from trajectories.