💡 The paper introduces SmolDocling, a compact vision-language model designed for end-to-end document conversion. The model aims to process entire pages and generate a new universal markup format called DocTags, which captures all page elements in their full context with location. Unlike existing approaches that rely on large foundational models or ensemble solutions, SmolDocling offers a single end-to-end conversion model with 256M parameters. This approach allows for accurately capturing content, structure, and spatial location of document elements.

The model is trained to reproduce document features such as code listings, tables, equations, charts, lists, and more across a diverse range of document types, including business documents, academic papers, technical reports, patents, and forms. The authors also contribute novel publicly sourced datasets for charts, tables, equations, and code recognition.

Experimental results demonstrate that SmolDocling performs competitively with other vision language models that are up to 27 times larger in size, while reducing computational requirements substantially. The model's compact size and robust performance make it a significant contribution to the field of document conversion. The authors plan to make the model and datasets publicly available, which will facilitate further research and development in this area. Overall, SmolDocling offers a efficient and effective solution for end-to-end document conversion, with potential applications in various industries and domains.