DISCO: Document Intelligence Suite for COmparative Evaluation
Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for COmparative Evaluation}, that evaluates optical character recognition (OCR) pipelines and vision-language models (VLMs) separately on parsing and question answering across diverse document types, including handwritten text, multilingual scripts, medical forms, infographics, and multi-page documents. Our evaluation shows that performance varies substantially across tasks and document characteristics, underscoring the need for complexity-aware approach selection. OCR pipelines are generally more reliable for handwriting an...
| Authors | Kenza Benkirane, Dan Goldwater, Martin Asenov et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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- kenza-ily/iam_discoDataset · cited by
- kenza-ily/visrbench_discoDataset · cited by
- kenza-ily/icdar_discoDataset · cited by
- kenza-ily/infographicvqa_discoDataset · cited by
- kenza-ily/docvqa_discoDataset · cited by
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Models and datasets referencing this paper
- kenza-ily/visrbench_discoDataset
- kenza-ily/icdar_discoDataset
- kenza-ily/infographicvqa_discoDataset
- kenza-ily/chartqapro_discoDataset
- kenza-ily/iam_discoDataset
- kenza-ily/docvqa_discoDataset
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