Structured Document

Structured document research focuses on efficiently extracting and representing information from diverse document formats, aiming to bridge the gap between unstructured data (like PDFs and images) and structured, machine-readable formats. Current research emphasizes developing robust models, including multimodal approaches and those leveraging graph convolutional networks and large language models, to handle complex layouts and diverse data types like text, tables, and images. This work is crucial for improving information retrieval, enabling advanced analytics across various domains, and facilitating the development of more reliable and efficient AI systems that can process and understand complex documents.