The DocLap integrated system for understanding multi-page scholarly documents

Computational understanding of documents is focused on visual and text analysis, building upon computer vision and natural language processing. With the advent of transformers document understanding is even more shifted from the actual comprehension of documents, which relies on concurrent perception of text, layout elements and document structure, to convoluted feature representations. Recent trends for providing unified access to such representations go towards Large Language Models (LLMs). However, these models have limitations: they lack explainability, demand significant resources for training and inference, and are not well suited for processing extensive inputs nor for direct application in specialized domains. This paper aims at creating a comprehensive interface for document analysis, enabling multi-layered exploration and integrating diverse features and contextual information. By bridging diverse information, our work pursues the identification, characterization, and linking of visual elements to semantic and contextual data, leveraging LLMs for interoperability. This enables a unified access to textual, visual, and structural layers, embedding levels of structured knowledge directly in the LLM context. Recent advances in Retrieval Augmented Generation (RAG) are also exploited to address some LLM limitations related to context length, allowing access to latent information from document representations such as graph and vector embeddings. The association of structural information to visual data allows formal analysis of documents and is exploited in our model to enhance visual recognition, improved through multi-modal LLM correction supported by ontology-based constraint violation detection. The framework enables semantic retrieval over extracted information, providing direct access to the document structure which can be exploited in many applications such as Question Answering (QA) and document understanding. As a result of this work, the DocLap (Document Layout Parser) system for document analysis and retrieval is proposed, which enables the extraction of visual and semantic features from documents and makes them accessible through natural language in an integrated framework providing conversational reasoning. The system’s segmentation, error detection, and information retrieval capabilities are extensively evaluated through experiments.