Database Paper Browser

Back to papers

Visual Segmentation for Information Extraction from Heterogeneous Visually Rich Documents

Summary: VS2 segments visually rich documents into logical blocks via document-type-agnostic cues. A distantly supervised search-and-select uses block boundaries to locate entities, outperforming text-only IE across three heterogeneous datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5679
Venue
SIGMOD
Year
2019
Pagerank
4.5061205e-05
Overall Rank
8,461 | 41.14%
DOI
10.1145/3299869.3319867

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers