README.md

metadata

license: other
task_categories:
  - image-to-text
language:
  - eng
  - zho
tags:
  - table-recognition
  - ocr
  - otsl
  - parquet
  - evaluation

Table Recognition Datasets (Mixed)

Evaluation data for table recognition (OCR → table markup), packaged as parquet shards with the images embedded inline.

Every table is stored in two dialects — OTSL and canonical HTML — so a model can be scored against either without re-deriving one.

Dataset	Rows	With image	Files	What it is
table_eval_paddleocr_vl	240,075	152,982	32 shards	The main evaluation set
table_eval_paddleocr_vl_excluded_toolong	3,828	3,633	4 shards	Samples withheld from evaluation because their tables are too long

Start with the first. The second is an archive of exclusions, not a scoring target.

Loading

import pyarrow.parquet as pq

t = pq.read_table("table_eval_paddleocr_vl/shard_0000.parquet")
layout_only = t.filter(t.column("has_numeric") == False).to_pylist()

Images are the original encoded bytes, never re-encoded — recompressing would introduce artefacts that contaminate the metric. When serving many requests, resolve images lazily per request; do not call pq.read_table(shard).to_pylist() on an inference path, since it re-decodes an entire row group per request and collapses under concurrency.

Scoring

Normalise prediction and ground truth to canonical HTML before computing TEDS, so a model that answers in OTSL and one that answers in HTML are compared on equal terms. The full column reference and distributions are in the main dataset card.

For the SPRINT-MUSTARD family the ground truth is deliberately all-empty <td>, so only TEDS-S is meaningful there; its TEDS column reads artificially low.