IDP-Leaderboard
Collection
New datasets used in Intelligent Document Processing Leaderboard. https://idp-leaderboard.org/
•
8 items
•
Updated
•
4
Dataset Viewer
images
unknown | annotation
stringlengths 1.63k
6.04k
|
|---|---|
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"\", \"1\": \"\", \"2\": \"\", \"3\": \"\", \"4\": \"\", \"5\": \"Ucr70U\", \"(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"5WiORXax\", \"1\": \"\", \"2\": \"jS89V0n\", \"3\": \"\", \"4\": \"Cft\", \"5(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"m7WJ6D\", \"1\": \"\", \"2\": \"\", \"3\": \"URvaI2rmx\", \"4\": \"\", \"5\":(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"R7l70\", \"1\": \"QGm48Aj\", \"2\": \"q8rP04KhT\", \"3\": \"bFRGtHBu\", \"4\"(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"wZdTJ\", \"1\": \"\", \"2\": \"M1ZsP\", \"3\": \"IL17vEupjF\", \"4\": \"\", \(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"NDR\", \"1\": \"RaUmPoWgMQ\", \"2\": \"c28\", \"3\": \"fUggZD\", \"4\": \"\",(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"\", \"1\": \"08nK\", \"2\": \"\", \"3\": \"\", \"4\": \"\", \"5\": \"\", \"6\(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"\", \"1\": \"v8Rvv35Rpd\", \"2\": \"\", \"3\": \"W9RqC\", \"4\": \"\", \"5\":(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"\", \"1\": \"Bo3vysnzEp\", \"2\": \"SK7b\", \"3\": \"z7vKnb0T\", \"4\": \"\",(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"cDq5Lle\", \"1\": \"xl15395O3b\", \"2\": \"\", \"3\": \"HztN7SQs\", \"4\": \"(...TRUNCATED) |
End of preview. Expand
in Data Studio
This dataset is generated syhthetically to create tables with following characteristics:
- Empty cell percentage in following range [40,70] (Sparse)
- There is clear seperator between rows and columns (Structured).
- 15 <= num rows <= 30, 7 <= num columns <= 15 (Long)
Load the dataset
import io
import pandas as pd
from PIL import Image
def bytes_to_image(self, image_bytes: bytes):
return Image.open(io.BytesIO(image_bytes))
def parse_annotations(self, annotations: str) -> pd.DataFrame:
return pd.read_json(StringIO(annotations), orient="records")
test_data = load_dataset('nanonets/long_sparse_structured_table', split='test')
data_point = test_data[0]
image, gt_table = (
bytes_to_image(data_point["images"]),
parse_annotations(data_point["annotation"]),
)
- Downloads last month
- 29
Size of downloaded dataset files:
19 MB
Size of the auto-converted Parquet files:
19 MB
Number of rows:
50