MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding
Optical character recognition (OCR) has evolved from line-level transcription to structured document parsing, requiring models to recover long-form sequences containing layout, tables, and formulas. Despite recent advances in vision-language models, most existing systems rely on autoregressive decoding, which introduces sequential latency and amplifies error propagation in long documents. In this work, we revisit document OCR from an inverse rendering perspective, arguing that left-to-right causal generation is an artifact of serialization rather than an intrinsic property of the task. Motivated by this insight, we propose MinerU-Diffusion, a unified diffusion-based framework that replaces autoregressive sequential decoding with parallel diffusion denoising under visual conditioning. MinerU-Diffusion employs a block-wise diffusion decoder and an uncertainty-driven curriculum learning strategy to enable stable training and efficient long-sequence inference. Extensive experiments demonstrate that MinerU-Diffusion consistently improves robustness while achieving up to 3.2x faster decoding compared to autoregressive baselines. Evaluations on the proposed Semantic Shuffle benchmark further confirm its reduced dependence on linguistic priors and stronger visual OCR capability.
69c3499395b48f16a30077d0
Hejun Dong
69c3499395b48f16a30077d1
Junbo Niu
69c3499395b48f16a30077d2
Bin Wang
claimed_verified
2026-04-07T08:47:39.661Z
63ae9ff5557befe297a76f90
https://cdn-avatars.huggingface.co/v1/production/uploads/1672388558183-noauth.jpeg
Bin Wang
wanderkid
user
wanderkid
69c3499395b48f16a30077d3
Weijun Zeng
69c3499395b48f16a30077d4
Wentao Zhang
69c3499395b48f16a30077d5
Conghui He
MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding
Optical character recognition (OCR) has evolved from line-level transcription to structured document parsing, requiring models to recover long-form sequences containing layout, tables, and formulas. Despite recent advances in vision-language models, most existing systems rely on autoregressive decoding, which introduces sequential latency and amplifies error propagation in long documents. In this work, we revisit document OCR from an inverse rendering perspective, arguing that left-to-right causal generation is an artifact of serialization rather than an intrinsic property of the task. Motivated by this insight, we propose MinerU-Diffusion, a unified diffusion-based framework that replaces autoregressive sequential decoding with parallel diffusion denoising under visual conditioning. MinerU-Diffusion employs a block-wise diffusion decoder and an uncertainty-driven curriculum learning strategy to enable stable training and efficient long-sequence inference. Extensive experiments demonstrate that MinerU-Diffusion consistently improves robustness while achieving up to 3.2x faster decoding compared to autoregressive baselines. Evaluations on the proposed Semantic Shuffle benchmark further confirm its reduced dependence on linguistic priors and stronger visual OCR capability.
MinerU-Diffusion is a diffusion-based framework that replaces autoregressive decoding with parallel diffusion denoising for document OCR, improving robustness and decoding speed.
diffusion-based framework autoregressive decoding parallel diffusion denoising block-wise diffusion decoder uncertainty-driven curriculum learning inverse rendering perspective vision-language models long-form sequences layout recovery table recovery formula recovery