OvisOCR2 Technical Report

Hugging Face Daily Papers Models
document-parsing ocr markdown end-to-end small-model reinforcement-learning distillation

Summary

OvisOCR2 is a 0.8B parameter end-to-end document parsing model that converts document page images to Markdown, achieving state-of-the-art scores on public benchmarks through a combination of supervised fine-tuning, reinforcement learning, and model fusion.

We introduce OvisOCR2, a 0.8B document parsing model. OvisOCR2 is designed as an end-to-end parser: given a document page image, it generates a Markdown representation in natural reading order, covering text, formulas, tables, and visual regions. We build a data engine that combines filtered real-document annotations with synthetic pages whose rendered images and Markdown targets are derived from the same HTML source. The training recipe includes supervised fine-tuning, reinforcement learning on a 4B branch with a multi-component reward design, on-policy distillation into the 0.8B model, and model fusion. On OmniDocBench v1.6, OvisOCR2 achieves a state-of-the-art overall score of 96.58, placing an end-to-end model at the top of this leaderboard previously dominated by pipeline methods and highlighting the potential of end-to-end document parsing. On PureDocBench, OvisOCR2 also achieves the highest Avg3 score of 75.06. Beyond these two public benchmarks, we evaluate OvisOCR2 on an in-house benchmark designed to cover a broader set of long-tail and challenging scenarios. OvisOCR2 obtains the best overall performance among the compared methods, providing further evidence of its generalization and robustness. OvisOCR2 is available at https://huggingface.co/ATH-MaaS/OvisOCR2.
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Paper page - OvisOCR2 Technical Report

Source: https://huggingface.co/papers/2607.13639 Authors:

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Abstract

WeintroduceOvisOCR2,a0.8Bdocumentparsingmodel.OvisOCR2isdesignedasanend-to-endparser:givenadocumentpageimage,itgeneratesaMarkdownrepresentationinnaturalreadingorder,coveringtext,formulas,tables,andvisualregions.Webuildadataenginethatcombinesfilteredreal-documentannotationswithsyntheticpageswhoserenderedimagesandMarkdowntargetsarederivedfromthesameHTMLsource.Thetrainingrecipeincludessupervisedfine-tuning,reinforcementlearningona4Bbranchwithamulti-componentrewarddesign,on-policydistillationintothe0.8Bmodel,andmodelfusion.OnOmniDocBenchv1.6,OvisOCR2achievesastate-of-the-artoverallscoreof96.58,placinganend-to-endmodelatthetopofthisleaderboardpreviouslydominatedbypipelinemethodsandhighlightingthepotentialofend-to-enddocumentparsing.OnPureDocBench,OvisOCR2alsoachievesthehighestAvg3scoreof75.06.Beyondthesetwopublicbenchmarks,weevaluateOvisOCR2onanin-housebenchmarkdesignedtocoverabroadersetoflong-tailandchallengingscenarios.OvisOCR2obtainsthebestoverallperformanceamongthecomparedmethods,providingfurtherevidenceofitsgeneralizationandrobustness.OvisOCR2isavailableathttps://huggingface.co/ATH-MaaS/OvisOCR2.

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#### ATH-MaaS/OvisOCR2 Image-Text-to-Text• 0.9B• Updatedabout 3 hours ago • 745 • 121

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