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Visual Template Inference for Data Extraction from Documents

Summary: TWIX infers latent visual templates for programmatically generated documents by clustering consistently co-located fields and enforcing alignment constraints (e.g., column/header and key/value alignment) to assemble templates for extraction. Template-driven extraction achieves >25% higher precision/recall than Evaporate/Textract/Azure/GPT-4-Vision on 34 datasets and is massively more scalable (≈520× faster, ≈3,786× cheaper) on large corpora. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7626
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,414 | 28.56%
DOI
10.1145/3769840

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BibTeX Citation

@inproceedings{lin_sigmod26,
        title = {{Visual Template Inference for Data Extraction from Documents}},
        author = {Lin, Yiming and Hasan, Mawil and Kosalge, Rohan and Cheung, Alvin and Parameswaran, Aditya G.},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769840},
        url = {https://dl.acm.org/doi/10.1145/3769840},
        year = {2026}
}

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