Papers › DocVQA: A Dataset for VQA on Document Images
DocVQA: A Dataset for VQA on Document Images
Minesh Mathew, Dimosthenis Karatzas, C. V. Jawahar
We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa.org
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering (VQA) | DocVQA test | Human | ANLS | 0.9436 | #1 of 33 | Archive leaderboard | report |
| Visual Question Answering (VQA) | DocVQA test | BERT_LARGE_SQUAD_DOCVQA_FINETUNED_Baseline | ANLS | 0.665 | #30 of 33 | Archive leaderboard | report |
| Visual Question Answering (VQA) | DocVQA test | BERT_LARGE_SQUAD_DOCVQA_FINETUNED_Baseline | Accuracy | 55.77 | #30 of 33 | Archive leaderboard | report |
| Visual Question Answering (VQA) | DocVQA val | BERT LARGE Baseline | Accuracy | 54.48 | #1 of 2 | Archive leaderboard | report |
| Visual Question Answering (VQA) | DocVQA val | đm bk | bk lôn | 0.655 | #2 of 2 | 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.
Methods
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