Page Document VQA

Page document visual question answering (DocVQA) focuses on developing AI systems that can accurately answer questions about the content of multi-page documents, integrating both visual and textual information. Current research emphasizes efficient processing of long documents, often employing transformer-based architectures with self-attention mechanisms or graph neural networks to model relationships between visual and textual elements, and mitigating issues like memorization of training data and language bias. These advancements are crucial for improving document understanding in various applications, ranging from assisting visually impaired individuals to enhancing information retrieval and knowledge extraction from complex documents.