Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
table-recognition
table-structure-recognition
document-image-analysis
ocr
document-understanding
teds
License:
| license: apache-2.0 | |
| pretty_name: Table Recognition Benchmark (17 sources, 184,074 tables) — canonical PaddleOCR-VL 1.6 OTSL | |
| tags: | |
| - table-recognition | |
| - table-structure-recognition | |
| - document-image-analysis | |
| - ocr | |
| - document-understanding | |
| - teds | |
| - paddleocr-vl | |
| language: | |
| - en | |
| - zh | |
| - ja | |
| - de | |
| - fr | |
| - ar | |
| - hi | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: test | |
| path: "shard_*.parquet" | |
| # Table Recognition Benchmark — canonical PaddleOCR-VL 1.6 OTSL | |
| 17 public and self-built table sources merged into **64 uniformly sized shards** | |
| (**184,074** tables, ~8.6 GB of images). Every row carries an OTSL ground truth | |
| in **PaddleOCR-VL 1.6's own OTSL convention**, the HTML ground truth, and a flag | |
| marking the tables that carry no cell text at all. | |
| This is a **corrected** release. The previous release | |
| (`CheneyWong/table_recognition_eval`) shipped an OTSL column whose rowspan | |
| structure did not follow PaddleOCR-VL's own parser, and whose documentation | |
| listed a token that **does not exist in the 1.6 vocabulary**. See | |
| [What changed](#what-changed-and-why) — in short, **42.6% of the rows have a | |
| different ground truth here**. | |
| ## Layout | |
| ``` | |
| shard_0000.parquet ... shard_0063.parquet # 64 evenly-sized shards | |
| scores_reference_base.parquet # reference run: PaddleOCR-VL 1.6 (base), per-sample scores | |
| scores_reference_sft_ckpt2800.parquet # reference run: our SFT ckpt-2800, per-sample scores | |
| README.md # you are here | |
| scripts/otsl_norm.py # the canonicaliser (HTML -> 1.6 OTSL) | |
| scripts/canonical_teds.py # the scorer used for the reference runs | |
| scripts/README.md # how to rebuild / verify | |
| ``` | |
| Shards are balanced by **total image bytes, not row count** — each holds | |
| ~169 MB of images, and every shard mixes 15–17 different sources, so a single | |
| shard already gives broad coverage. | |
| ## Schema | |
| | column | type | meaning | | |
| |---|---|---| | |
| | `bytes` | binary | the table image, as stored (PNG/JPEG) | | |
| | `source` | string | fine-grained original dataset name | | |
| | `benchmark` | string | which of the 17 sources this row came from | | |
| | `sample_id` | string | original sample identifier (may be empty) | | |
| | `rows` / `cols` | int64 | table grid dimensions | | |
| | `otsl` | string | **canonical** OTSL ground truth (PaddleOCR-VL 1.6 convention) | | |
| | `html` | string | HTML ground truth | | |
| | `resolution` | string | `WIDTHxHEIGHT` | | |
| | `width` / `height` | int64 | image dimensions | | |
| | `structure_only` | bool | true when the table has **no cell text at all** | | |
| | `otsl_prev` | string | the ground truth of the previous release (kept for reference) | | |
| | `otsl_changed` | bool | true when `otsl` differs from `otsl_prev` | | |
| ## What is OTSL | |
| **OTSL = Optimized Table Structure Language**, the compact token language | |
| PaddleOCR uses to represent table structure. A table is a flat token stream — | |
| no nesting, no closing tags. The vocabulary is **exactly six tokens** (this is | |
| PaddleOCR-VL 1.6's own tokenizer, `PaddleOCR-VL-1.6/tokenizer.json`): | |
| | token | id | meaning | | |
| |---|---:|---| | |
| | `<fcel>` | 101309 | a new (filled) cell starts here | | |
| | `<ecel>` | 101308 | an empty cell | | |
| | `<lcel>` | 101311 | continuation **to the left** (horizontal merge) | | |
| | `<ucel>` | 101312 | continuation **above** (vertical merge) | | |
| | `<xcel>` | 101310 | continuation both left and above | | |
| | `<nl>` | 101313 | end of row | | |
| Cell text is inlined directly after its `<fcel>`, so the stream alternates | |
| structure and content: | |
| ``` | |
| <fcel>Year<fcel>Division<ecel><nl> | |
| <fcel>2023<fcel>A<ecel><nl> | |
| ``` | |
| A **rowspan** is a continuation *row*, not a marker inside the cell: | |
| ``` | |
| <fcel>A<fcel>B<lcel><nl> A spans two rows, B spans two columns | |
| <ucel><fcel>C<fcel>D<nl> <- the continuation row, <ucel> holds A's column | |
| ``` | |
| ## What changed, and why | |
| The previous release's `otsl` column was produced by an HTML→OTSL converter | |
| that (a) put a rowspan's continuation marker **inside the same row** using a | |
| token `<ulcel>`, and (b) padded every row with `<ecel>` up to a fixed column | |
| count. | |
| **`<ulcel>` is not a PaddleOCR-VL 1.6 token.** It is not in the model's | |
| vocabulary, so the model can never emit it; and the usual scorer | |
| (`canonical_teds.py`, used by OmniDocBench-style table evaluation) does not list | |
| it among the tokens it recognises either, so it silently ends up **inside the | |
| cell text**: | |
| ``` | |
| <fcel><ulcel><ulcel>characteristic -> cell text = "<ulcel><ulcel>characteristic" | |
| ``` | |
| Rows containing `<ulcel>` in the previous release: | |
| | source | rows with `<ulcel>` | | |
| |---|---:| | |
| | pubtables1m | 37% | | |
| | ccocr | 33% | | |
| | omnidocbench16 | 25% | | |
| | pubtabnet_icdar21 | 23% | | |
| | tablet | 22% | | |
| | omnidocbench15 | 20% | | |
| | ocrbenchv2 | 17% | | |
| | scitsr | 16% | | |
| | fintabnet | 14% | | |
| This release rebuilds the ground truth from the **original HTML** following | |
| PaddleOCR-VL 1.6's own parser | |
| (`paddlex/inference/pipelines/paddleocr_vl/uilts.py::otsl_parse_texts`): | |
| * a cell starts at `<fcel>` (with text) or `<ecel>` (empty); | |
| * a **colspan** puts `<lcel>` in the same row, to the right; | |
| * a **rowspan** puts `<ucel>` in the row(s) **below**, in the same column; | |
| * a cell merged both ways is `<xcel>`; | |
| * rows are **not** padded — the row width is the row width. | |
| Where a source's HTML in the previous release was only a shell (all cells | |
| empty, the text living in the source annotation), the source annotation is kept | |
| instead of the HTML-derived one, so no text is ever lost. | |
| **Verification.** The canonical OTSL was parsed back with PaddleOCR-VL 1.6's own | |
| parser and compared cell by cell (text, rowspan, colspan) against the grid | |
| rebuilt from the HTML: **99.77% identical** (2,993 / 3,000 samples; the 7 | |
| differences are extra empty cells produced by the official parser's internal | |
| "pad every row to max_cols" step, not structural errors). | |
| ## Reference runs | |
| `scores_reference_*.parquet` hold the per-sample scores of two reference runs | |
| against **this** ground truth, so the numbers below are reproducible: | |
| | run | model | pooled TEDS-S | | |
| |---|---|---| | |
| | base | PaddleOCR-VL 1.6 (no fine-tuning) | see `scores_reference_base.parquet` | | |
| | sft_ckpt2800 | our SFT (checkpoint-2800) | see `scores_reference_sft_ckpt2800.parquet` | | |
| The scorer is `scripts/canonical_teds.py` (primary metric TEDS-S; note that for | |
| pure-structure tables, whose cells are all empty, only TEDS-S is meaningful). | |
| ## Intended use | |
| Training and evaluating table-structure recognition models on 17 public and | |
| self-built table sources. The images are cropped table images; the labels are | |
| structure + cell text. | |
| If you use this dataset, please also cite the original sources of the | |
| individual tables (the `source` column names them). | |