SmolDocling: An ultra-compact vision-language model for end-to-end
  multi-modal document conversion
We introduce SmolDocling, an ultra-compact vision-language model targeting
end-to-end document conversion. Our model comprehensively processes entire
pages by generating DocTags, a new universal markup format that captures all
page elements in their full context with location. Unlike existing approaches
that rely on large foundational models, or ensemble solutions that rely on
handcrafted pipelines of multiple specialized models, SmolDocling offers an
end-to-end conversion for accurately capturing content, structure and spatial
location of document elements in a 256M parameters vision-language model.
SmolDocling exhibits robust performance in correctly reproducing 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 -- significantly extending beyond
the commonly observed focus on scientific papers. Additionally, we contribute
novel publicly sourced datasets for charts, tables, equations, and code
recognition. Experimental results demonstrate that SmolDocling competes with
other Vision Language Models that are up to 27 times larger in size, while
reducing computational requirements substantially. The model is currently
available, datasets will be publicly available soon.
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Ahmed Nassar
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Ahmed Nassar
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Andres Marafioti
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Maksym Lysak
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Nikolaos Livathinos
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Nikos Livathinos
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Christoph Auer
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Rafael Teixeira de Lima
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A. Said Gurbuz
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Said Gurbuz
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Michele Dolfi
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Miquel Farré
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Miquel Farré
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Peter W. J. Staar
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Peter W. J. Staar
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SmolDocling: An ultra-compact vision-language model for end-to-end
  multi-modal document conversion
We introduce SmolDocling, an ultra-compact vision-language model targeting
end-to-end document conversion. Our model comprehensively processes entire
pages by generating DocTags, a new universal markup format that captures all
page elements in their full context with location. Unlike existing approaches
that rely on large foundational models, or ensemble solutions that rely on
handcrafted pipelines of multiple specialized models, SmolDocling offers an
end-to-end conversion for accurately capturing content, structure and spatial
location of document elements in a 256M parameters vision-language model.
SmolDocling exhibits robust performance in correctly reproducing 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 -- significantly extending beyond
the commonly observed focus on scientific papers. Additionally, we contribute
novel publicly sourced datasets for charts, tables, equations, and code
recognition. Experimental results demonstrate that SmolDocling competes with
other Vision Language Models that are up to 27 times larger in size, while
reducing computational requirements substantially. The model is currently
available, datasets will be publicly available soon.
SmolDocling is a compact vision-language model that performs end-to-end document conversion with robust performance across various document types using 256M parameters and a new markup format.
vision-language model DocTags end-to-end document conversion universal markup format page elements location context large foundational models ensemble solutions specialized models code listings tables equations charts lists diverse document types publicly sourced datasets Vision Language Models computational requirements