The Case for HTML as the Canonical Representation in Document AI

In the realm of document AI, the use of HTML as the canonical representation layer is advocated for its ability to maintain high fidelity, semantic richness, and reliability in document processing, as opposed to traditional formats like JSON or markdown. HTML captures essential document elements with precision, supports semantic granularity through native elements and attributes, aligns with the training of vision-language models, and offers broad interoperability and flexibility. The approach leverages a 70-element ontology to ensure comprehensive document understanding and employs a multimodal strategy for processing documents efficiently. This methodology facilitates precise data retrieval, compliance, and auditability, with HTML enabling a visually and semantically accurate reconstruction of source documents. By championing HTML, the aim is to enhance document AI systems' accuracy and efficiency, grounding them in a structure that aligns with modern machine learning models and enterprise requirements.

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