Datasets:

CheneyWong
/

table_recognition_eval_canonical

# scripts/

## `otsl_norm.py` — the canonicaliser
HTML → **PaddleOCR-VL 1.6's own OTSL convention**. This is what produced the
`otsl` column of the shards.
```python
import sys; sys.path.insert(0, ".")
from otsl_norm import html_to_otsl

otsl = html_to_otsl(html)          # canonical OTSL
```
Rules it implements (they mirror
`paddlex/inference/pipelines/paddleocr_vl/uilts.py::otsl_parse_texts`, the
parser PaddleOCR-VL 1.6 uses at inference time):
* cell origin → `<fcel>` + text, or `<ecel>` when the cell is empty;
* colspan → `<lcel>` in the **same row**, to the right;
* rowspan → `<ucel>` in the row(s) **below**, in the same column;
* merged both ways → `<xcel>`;
* rows are **not** padded — a row lists the cells it actually has;
* cell text: leading/trailing whitespace stripped, internal whitespace runs
  collapsed to a single space.
Self-check (parses its own output back with the official parser and compares
cell by cell):
```bash
python3 validate_otsl_norm.py <pack_dir> 3000   # -> 99.77% identical
```
## `canonical_teds.py` — the scorer
The table scorer used for the reference runs. Primary metric is **TEDS-S**;
`text_profile="canonical"` (the default) flattens tags, decodes entities,
converts LaTeX to Unicode and collapses whitespace on both sides.
Note: for *pure-structure* tables (every cell empty, e.g. `sprint_mustard`)
TEDS is undefined and only TEDS-S is meaningful.
## `build_norm_pack.py` — rebuild the ground truth of a pack
```bash
python3 build_norm_pack.py --src <old_pack> --out <new_pack> --workers 8
```
Rebuilds the `otsl` column from the `html` column. It keeps the **source**
OTSL instead whenever the HTML in that pack is only a shell (all cells empty,
the text living in the source annotation) — otherwise the text would be lost.
Set `POVL_` env vars are not needed here; the rule is hard-coded and logged
per shard.
## `compare_runs.py` — paired comparison of two runs
```bash
python3 compare_runs.py --a <run_tag> --b <run_tag> --out report.json
```
Pairs the two runs **by sample id** and reports per-cohort deltas with a
paired bootstrap CI. Use the `合计(配对同 id, n=…)` line for the headline
number; the `合计(按样本)` line compares A's own sample set against B's own
sample set, which mixes two different populations when the two runs were
scored on different subsets.

	# scripts/

	## `otsl_norm.py` — the canonicaliser

	HTML → PaddleOCR-VL 1.6's own OTSL convention. This is what produced the
	`otsl` column of the shards.

	```python
	import sys; sys.path.insert(0, ".")
	from otsl_norm import html_to_otsl

	otsl = html_to_otsl(html) # canonical OTSL
	```

	Rules it implements (they mirror
	`paddlex/inference/pipelines/paddleocr_vl/uilts.py::otsl_parse_texts`, the
	parser PaddleOCR-VL 1.6 uses at inference time):

	* cell origin → `<fcel>` + text, or `<ecel>` when the cell is empty;
	* colspan → `<lcel>` in the same row, to the right;
	* rowspan → `<ucel>` in the row(s) below, in the same column;
	* merged both ways → `<xcel>`;
	* rows are not padded — a row lists the cells it actually has;
	* cell text: leading/trailing whitespace stripped, internal whitespace runs
	collapsed to a single space.

	Self-check (parses its own output back with the official parser and compares
	cell by cell):

	```bash
	python3 validate_otsl_norm.py <pack_dir> 3000 # -> 99.77% identical
	```

	## `canonical_teds.py` — the scorer

	The table scorer used for the reference runs. Primary metric is TEDS-S;
	`text_profile="canonical"` (the default) flattens tags, decodes entities,
	converts LaTeX to Unicode and collapses whitespace on both sides.

	Note: for pure-structure tables (every cell empty, e.g. `sprint_mustard`)
	TEDS is undefined and only TEDS-S is meaningful.

	## `build_norm_pack.py` — rebuild the ground truth of a pack

	```bash
	python3 build_norm_pack.py --src <old_pack> --out <new_pack> --workers 8
	```

	Rebuilds the `otsl` column from the `html` column. It keeps the source
	OTSL instead whenever the HTML in that pack is only a shell (all cells empty,
	the text living in the source annotation) — otherwise the text would be lost.
	Set `POVL_` env vars are not needed here; the rule is hard-coded and logged
	per shard.

	## `compare_runs.py` — paired comparison of two runs

	```bash
	python3 compare_runs.py --a <run_tag> --b <run_tag> --out report.json
	```

	Pairs the two runs by sample id and reports per-cohort deltas with a
	paired bootstrap CI. Use the `合计(配对同 id, n=…)` line for the headline
	number; the `合计(按样本)` line compares A's own sample set against B's own
	sample set, which mixes two different populations when the two runs were
	scored on different subsets.