Papers › DocFormer: End-to-End Transformer for Document Understanding
DocFormer: End-to-End Transformer for Document Understanding
Srikar Appalaraju, Bhavan Jasani, Bhargava Urala Kota, Yusheng Xie, R. Manmatha
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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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Document Image Classification | RVL-CDIP | DocFormerBASE | Accuracy | 96.17% | #3 of 31 | Archive leaderboard | report |
| Document Image Classification | RVL-CDIP | DocFormerBASE | Parameters | 183M | #3 of 31 | Archive leaderboard | report |
| Document Image Classification | RVL-CDIP | DocFormer large | Accuracy | 95.50% | #8 of 31 | Archive leaderboard | report |
| Document Image Classification | RVL-CDIP | DocFormer large | Parameters | 536M | #8 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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