Model card
Hierarchical Table Recognizer.
Takaya Kawakatsutable-recognition
Non-causal attention with parallel inference algorithm for fast table cell content recognition. arXiv:2512.21083 (Dec 2025).
§ 01 · Benchmarks
Every benchmark Hierarchical Table Recognizer has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | table-recognition-challenge-mini-test | Computer Vision · Table Recognition | teds-all-samples | 96.6% | #1 | — | source ↗ |
| 02 | table-recognition-challenge-mini-test | Computer Vision · Table Recognition | teds-complex-samples | 95.1% | #1 | — | source ↗ |
| 03 | table-recognition-challenge-mini-test | Computer Vision · Table Recognition | teds-simple-samples | 98.1% | #2 | — | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 02 · Strengths by area
Where Hierarchical Table Recognizer actually performs.
§ 05 · Sources & freshness
Where these numbers come from.
paper
3
results
0 of 3 rows marked verified.