Datasets:
License:
| license: other | |
| task_categories: [image-to-text, table-to-text] | |
| language: | |
| - as | |
| - bn | |
| - gu | |
| - hi | |
| - kn | |
| - ml | |
| - or | |
| - pa | |
| - ta | |
| - te | |
| - ur | |
| - zh | |
| - en | |
| tags: | |
| - table | |
| - html | |
| - image-to-html | |
| - table-structure | |
| - structure-only | |
| - SPRINT-MUSTARD | |
| # SPRINT-MUSTARD | |
| 1429 table image/HTML pairs. **Test split only** -- the source dataset publishes | |
| no train partition, so this is a benchmark, not a training set. | |
| ## Ground truth is structure-only -- report TEDS-S, not TEDS | |
| Every `<td>` in the HTML is **empty**. The annotation expresses the grid and the | |
| rowspan/colspan merge relationships and nothing else; the source carries no cell | |
| text. | |
| TEDS compares cell text as well as structure. Against an all-empty ground truth | |
| it degenerates into measuring how well a model reproduces nothing, so **TEDS is | |
| not meaningful on this dataset and only TEDS-S should be reported.** | |
| This is the opposite of TABLET, whose cells do carry values and where TEDS is | |
| the right metric. | |
| ## What is in here | |
| `SPRINT-MUSTARD_html_image_pairs.zip` contains a single `test/` directory with | |
| 1429 pairs, one `{language}_{n}.png` and one `{language}_{n}.html` each. | |
| The language prefix prevents filename collisions across languages (`hindi_1.png` | |
| vs `tamil_1.png`). | |
| Breakdown: 1215 pairs over 12 Indic languages, plus 214 `scenetext` and the | |
| chinese/english sets already present in the source. | |
| ## Files | |
| | file | what | | |
| |---|---| | |
| | `SPRINT-MUSTARD_html_image_pairs.zip` | the 1429 image/HTML pairs (0.56 GB) | | |
| | `SPRINT-MUSTARD_html_image_pairs_说明.txt` | the source dataset's own description, kept verbatim | | |
| ## Intended use | |
| Evaluating table **structure** recognition on low-resource scripts, where cell | |
| text transcription is out of scope. Pair it with a text-bearing set such as | |
| TABLET if you need content accuracy. | |