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DocFormer: End-to-End Transformer for Document Understanding

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arxiv 2106.11539 v2 pith:223ZEFVP submitted 2021-06-22 cs.CV

Srikar Appalaraju , Bhavan Jasani , Bhargava Urala Kota , Yusheng Xie , R. Manmatha This is my paper · ORCID
classification cs.CV
keywords docformermulti-modaldocumentspatialtextthemtransformerunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal
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We present DocFormer -- a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). VDU is a challenging problem which aims to understand documents in their varied formats (forms, receipts etc.) and layouts. In addition, DocFormer is pre-trained in an unsupervised fashion using carefully designed tasks which encourage multi-modal interaction. DocFormer uses text, vision and spatial features and combines them using a novel multi-modal self-attention layer. DocFormer also shares learned spatial embeddings across modalities which makes it easy for the model to correlate text to visual tokens and vice versa. DocFormer is evaluated on 4 different datasets each with strong baselines. DocFormer achieves state-of-the-art results on all of them, sometimes beating models 4x its size (in no. of parameters).

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence

    cs.CV 2025-02 conditional novelty 6.0 of 10

    Granite Vision is a ~3B parameter open-weights vision-language model that reaches state-of-the-art scores on document understanding benchmarks despite its small size.

  2. Vector embedding of multi-modal texts: a tool for discovery?

    cs.IR 2025-09 conditional novelty 4.0 of 10

    Using ColPali embeddings of 3,600 textbook page images, cosine similarity beats dot product, Euclidean, and Manhattan distances on top-5 retrieval, but only reaches 0.51 precision@5 without a text-only baseline.

  3. Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends

    cs.CL 2025-01 conditional novelty 3.0 of 10

    A structured overview of question answering over visually rich documents, comparing encoding, vision-only, and multi-page methods, and highlighting comparability issues in existing benchmarks.

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