pretty_name: Canonical Table Recognition Training by Source
tags:
- table-recognition
- table-structure-recognition
- ocr
- paddleocr-vl
language:
- en
- zh
configs:
- config_name: default
data_files:
- split: train
path:
- FinTabNet_OTSL/train.parquet
- MMSci/train.parquet
- PubTabNet/train.parquet
- PubTables-1M/train.parquet
- PubTables-v2/train.parquet
- SciTSR/train.parquet
- SynthTabNet/train.parquet
- TABLET/train.parquet
- synth_2d/train.parquet
- config_name: FinTabNet_OTSL
data_files:
- split: train
path: FinTabNet_OTSL/train.parquet
- config_name: MMSci
data_files:
- split: train
path: MMSci/train.parquet
- config_name: PubTabNet
data_files:
- split: train
path: PubTabNet/train.parquet
- config_name: PubTables-1M
data_files:
- split: train
path: PubTables-1M/train.parquet
- config_name: PubTables-v2
data_files:
- split: train
path: PubTables-v2/train.parquet
- config_name: SciTSR
data_files:
- split: train
path: SciTSR/train.parquet
- config_name: SynthTabNet
data_files:
- split: train
path: SynthTabNet/train.parquet
- config_name: TABLET
data_files:
- split: train
path: TABLET/train.parquet
- config_name: synth_2d
data_files:
- split: train
path: synth_2d/train.parquet
Table recognition training — one source, one Parquet
The frozen training pool contains 1,454,538 samples across 9 sources. 9/9 source folders are uploaded and remotely verified.
Each source has its own folder with exactly one train.parquet, audit.json and README.md. Original encoded image bytes are embedded; there is no dependency on local ZIP files.
| Source / folder | Samples | Upload status |
|---|---|---|
| FinTabNet_OTSL | 81,032 | verified |
| MMSci | 33,336 | verified |
| PubTabNet | 468,698 | verified |
| PubTables-1M | 728,203 | verified |
| PubTables-v2 | 104,219 | verified |
| SciTSR | 8,228 | verified |
| SynthTabNet | 10,152 | verified |
| TABLET | 16,822 | verified |
| synth_2d | 3,848 | verified |
Schema
| Field | Meaning |
|---|---|
| bytes | Original encoded image, unchanged |
| width / height | Actual image dimensions read from image bytes |
| otsl | Authoritative reviewed-pool OTSL, unchanged |
| html | Derived from OTSL; entities decoded and cell text HTML-escaped; not a model prediction |
| rows / cols | Logical table grid dimensions reconstructed from OTSL |
| source / sample_id / document_id | Source and stable sample/document identity |
| image_sha256 / otsl_sha256 | Integrity checks against the audited training master |
| structure_only | No cell text; structure-only supervision |
| model_budget_eligible | Whether the sample fits the current model token budget; all valid pool rows are retained |
| html_origin | HTML conversion provenance |
TabRecSet zh/en, cxrender, cxrender_zh, WTW and real-scene training data are excluded according to the active pool policy. Upstream split and held-out/evaluation exclusions are preserved. This is the unified pool from which SFT/RL/OPD selections are made, rather than one stage's selected subset. Annotation auditing does not mean every image was manually reviewed.
Loading
from datasets import load_dataset
from PIL import Image
import io
ds = load_dataset("CheneyWong/table_recognition_train_canonical", "PubTables-1M", split="train")
sample = ds[0]
image = Image.open(io.BytesIO(sample["bytes"]))
# Default config combines all uploaded sources.
Each source audit includes the Parquet SHA256, sample count and originating master SHA256. All image and OTSL hashes are checked after writing Parquet, and every uploaded Parquet is compared with its remote LFS SHA256. No resizing, re-encoding, new training or model inference is performed during this export.
Original datasets retain their provenance and original terms; this repository reorganizes the supplied audited training pool.
PaddleOCR-VL-1.6 training-pool evaluation
paddleocrvl1.6_eval.json is the single aggregate JSON containing provenance, metric definitions, per-source coverage and a samples array covering every training Parquet row.
175,681/1,454,538 training samples currently have TEDS-S scores; 1,278,857 have no cached inference. These are original PaddleOCR-VL-1.6 base-model greedy screening results, never SFT/RL/OPD checkpoint results. The scored subset was selected for screening and is not an unbiased full-pool benchmark.
JSON fields include source, sample_id, gt_sha256, teds, teds_s, status, scoring_status and finish_reason where applicable. Join by sample_id within each source and verify gt_sha256 == otsl_sha256. not_evaluated means both scores are null, not zero. For GT without cell text, teds=null and only TEDS-S applies. Numeric metrics are in [0,1]. Scoring uses the project canonical TEDS implementation with reviewed GT and text normalization; it is not the separate official fullpage benchmark. Training Parquets and original images are unchanged.
import json
from huggingface_hub import hf_hub_download
p = hf_hub_download("CheneyWong/table_recognition_train_canonical", "paddleocrvl1.6_eval.json", repo_type="dataset")
evaluation = json.load(open(p))
by_id = {r["sample_id"]: r for r in evaluation["samples"]}