Datasets:
bytes unknown | source stringclasses 6
values | benchmark stringclasses 4
values | sample_id stringlengths 0 90 | rows int64 1 95 | cols int64 1 100 | otsl stringlengths 33 17.4k | html stringlengths 62 28.2k | resolution stringlengths 6 9 | width int64 159 4.96k | height int64 30 7.01k | structure_only bool 1
class |
|---|---|---|---|---|---|---|---|---|---|---|---|
"iVBORw0KGgoAAAANSUhEUgAACawAAAQ/CAIAAAB9o670AAEAAElEQVR4nOy9dbxsV3n//3nWWlvHjp/rcQ8kQJCEUoIFKAluJRS(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | eastmoney_7cae1642250a3dbabfef7624d3424ec6e162ae91c8de4acfa89a9779092b0189.pdf_16.jpg | 6 | 10 | "<fcel>企业类型<fcel>目的<fcel>模式和特点<fcel>优势<fcel>典型企业<ecel><ecel><ecel>(...TRUNCATED) | "<table border=\"1\"><tr><td>企业类型</td><td>目的</td><td>模式和特点</td><td>优势</td>(...TRUNCATED) | 2476x1087 | 2,476 | 1,087 | false |
"iVBORw0KGgoAAAANSUhEUgAAA8kAAAIJCAIAAAAZMejOAAEAAElEQVR4nOz9WbNlR3YmiH1rufueznjHuDEjAlMCOWdySrJIFqt(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | eastmoney_d09a006aa02ddc09299bbb9a1b5efa0d77408191f0c1ff1fca8c80bd6150f806.pdf_17.jpg | 8 | 10 | "<fcel><fcel>RPCB多层板<fcel>软板+模组<fcel>HDI<fcel>IC载板<ecel><ecel><ecel><ecel><ecel><n(...TRUNCATED) | "<table border=\"1\"><tr><td></td><td>RPCB多层板</td><td>软板+模组</td><td>HDI</td><td>IC载(...TRUNCATED) | 969x521 | 969 | 521 | false |
"iVBORw0KGgoAAAANSUhEUgAABhYAAAHjCAIAAABAS7IxAAEAAElEQVR4nOT9268s2ZkfiP2+b90iIjP33udaVSyyilW8FO9kt1p(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | yanbaopptmerge_6b6ed54523980841c1a28a656d0295ec638cf50c991c16cafec715237c83919f.pdf_31.jpg | 15 | 22 | "<fcel>栏目<fcel>2016A<fcel>2017A<fcel>2018A<fcel>2019A<fcel>2020A<fcel>2021A<fcel>2022A<fcel>2023(...TRUNCATED) | "<table border=\"1\"><tr><td>栏目</td><td>2016A</td><td>2017A</td><td>2018A</td><td>2019A</td><td>(...TRUNCATED) | 1558x483 | 1,558 | 483 | false |
"iVBORw0KGgoAAAANSUhEUgAABdUAAAJPCAIAAAAVF6qaAAEAAElEQVR4nOydd5xU5fX/n3vv3LnTZzuw9CJiFwtoBMXeNfYSRU3(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | yanbaopptmerge_PattPatelCh12.pdf_27.jpg | 5 | 8 | "<fcel>Name<fcel>Type<fcel>Offset<fcel>Scope<ecel><ecel><ecel><ecel><nl><fcel>inGlobal<fcel>int<fcel(...TRUNCATED) | "<table border=\"1\"><tr><td>Name</td><td>Type</td><td>Offset</td><td>Scope</td></tr><tr><td>inGloba(...TRUNCATED) | 1493x591 | 1,493 | 591 | false |
"iVBORw0KGgoAAAANSUhEUgAACS4AAAUTCAIAAABJSrkxAAEAAElEQVR4nOzdd5xU9fU//vft/U6f7bsgVSlRUaJi10/UGLtisIE(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | yanbaopptmerge_yanbaoPPT_1020.jpg | 9 | 6 | "<fcel><lcel>描写手法<fcel>具体分析<ecel><ecel><nl><fcel><ulcel><ulcel>正面描写<fcel>类(...TRUNCATED) | "<table border=\"1\"><tr><td colspan=\"2\">描写手法</td><td>具体分析</td></tr><tr><td rowspa(...TRUNCATED) | 2350x1299 | 2,350 | 1,299 | false |
"iVBORw0KGgoAAAANSUhEUgAAByQAAAM+CAIAAADU/CEsAAEAAElEQVR4nMz9Ta8sy5IdiK1lHrn3OffdUjWfSIACGoQGIjThjOM(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | yanbaopptmerge_yanbaoPPT_1695.jpg | 4 | 6 | "<fcel><lcel>实验过程<fcel>描述听到的声音的强弱变化<ecel><ecel><nl><fcel><ulcel>实(...TRUNCATED) | "<table border=\"1\"><tr><td colspan=\"2\">实验过程</td><td>描述听到的声音的强弱变化(...TRUNCATED) | 1828x830 | 1,828 | 830 | false |
"iVBORw0KGgoAAAANSUhEUgAABTcAAADqCAIAAAD4TuaiAABfvklEQVR4nO3dd5wUZbY38KdydZ6eAENWQDACCoIYSCoq4KrIdXW(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | yanbaopptmerge_yanbaoPPT_3395.jpg | 2 | 4 | "<fcel>我的人生理想<fcel>①____▲____<ecel><ecel><nl><fcel>我的行动计划<fcel>②____(...TRUNCATED) | "<table border=\"1\"><tr><td>我的人生理想</td><td>①____▲____</td></tr><tr><td>我的行动(...TRUNCATED) | 1335x234 | 1,335 | 234 | false |
"iVBORw0KGgoAAAANSUhEUgAAA0YAAADcCAIAAADbUyb3AAEAAElEQVR4nOz9ebBs21kfCP6+bw17yswz3vG9d5+kJ5CEJIQmEGB(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | yanbaopptmerge_yanbaoPPT_4275.jpg | 4 | 8 | "<fcel><ulcel>平行四边形<fcel>底<fcel>高<fcel>面积<ecel><ecel><ecel><ecel><nl><fcel>6m<fcel(...TRUNCATED) | "<table border=\"1\"><tr><td rowspan=\"2\">平行四边形</td><td>底</td><td>高</td><td>面积</t(...TRUNCATED) | 838x220 | 838 | 220 | false |
"iVBORw0KGgoAAAANSUhEUgAABrUAAAOBCAIAAADQh7ybAAEAAElEQVR4nOz9ebysSVbXC/9WRDxjDns6Y1XPLTbQtCAyXERBVLz(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | yanbaopptmerge_yanbaoPPT_4285.jpg | 4 | 8 | "<fcel><fcel>骨内无机物含量<fcel>骨内有机物含量<fcel>特性<ecel><ecel><ecel><ecel><nl(...TRUNCATED) | "<table border=\"1\"><tr><td></td><td>骨内无机物含量</td><td>骨内有机物含量</td><td>(...TRUNCATED) | 1717x897 | 1,717 | 897 | false |
"iVBORw0KGgoAAAANSUhEUgAACKIAAASACAIAAABJeOAnAAEAAElEQVR4nOz9d7ht11kf+n/fUWZdfbezT1WXLNmSbblbNgYbMBh(...TRUNCATED) | OmniDocBench-v1.5 | omnidocbench15 | yanbaor2_4862cd850679e945a98c54c8d4c37be0154b5790d2c32da35d0f5ca0be413693.pdf_9.jpg | 4 | 8 | "<fcel>处罚情形<fcel>主要违法违规事由<fcel>罚单数量（张）<fcel>机构示例<ecel>(...TRUNCATED) | "<table border=\"1\"><tr><td>处罚情形</td><td>主要违法违规事由</td><td>罚单数量（(...TRUNCATED) | 2210x1152 | 2,210 | 1,152 | false |
Rebuilt Table Sources — OmniDocBench / CC-OCR / OCRBench-v2
1,635 table images with OTSL and HTML ground truth, covering the four sources of the merged benchmark whose tables had to be rebuilt from upstream annotations rather than read from a published image+html pair.
| benchmark | rows | origin |
|---|---|---|
omnidocbench16 |
665 | OmniDocBench v1.6 |
omnidocbench15 |
512 | OmniDocBench v1.5 |
ccocr |
300 | CC-OCR |
ocrbenchv2 |
158 | OCRBench-v2 table-parsing tasks with an explicit bbox |
| total | 1,635 |
Provenance
OmniDocBench v1.5 / v1.6 — the releases ship page images plus a JSON whose
table entries carry a poly and an html field. Every entry with
category_type == "table" contributes one crop, taken with its poly plus a
10px margin so cell borders survive. Ground truth is the entry's own html;
OTSL is derived from it. Note that opendatalab/OmniDocBench on Hugging Face
publishes only the v1.5 JSON — v1.6 was built from the local copy.
CC-OCR — 300 tables, four subsets of 75: photo_chn, photo_eng,
scan_chn, scan_eng. HTML ships with the release; OTSL is derived from it.
OCRBench-v2 — this is a general document-VQA benchmark, so most of it is
unusable for table recognition. Of its 700 table parsing tasks, the ones that
name an explicit region in the prompt
("... in the region [x1,y1,x2,y2] of the image", normalised to 0–1000) are
the only ones with a croppable table — 158 of them, which is this set. The
other 542 say "all tables in the image" and were left out.
Ground truth is the answers[0] field. 19 of the 158 rows are Markdown
tables rather than HTML; for those the html column holds the source text
verbatim and otsl was produced from it with a Markdown-aware converter.
Schema
| column | type | meaning |
|---|---|---|
bytes |
binary | the cropped table image |
source |
string | original dataset name |
benchmark |
string | one of the four above |
sample_id |
string | upstream identifier |
rows / cols |
int64 | table grid dimensions |
otsl |
string | OTSL ground truth |
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 |
Scoring
TEDS = 1 - TD / max(N_pred, N_gt) over the table tree; TEDS-S is the
structure-only variant. When structure_only is true every cell is empty and
only the grid is annotated, so TEDS is not computed for those rows — only
TEDS-S is.
Related
CheneyWong/table_recognition_eval— the full merged benchmark, 184,074 tables across 17 sourcesCheney20/paddleocr-vl-table-recognition— scoring code and GT converters
Citation
Please cite the original datasets. This repository only repackages them under a unified schema.
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