IDP-Leaderboard
Collection
New datasets used in Intelligent Document Processing Leaderboard. https://idp-leaderboard.org/ • 8 items • Updated • 6
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... | {"Col_1": {"0": "VNw0", "1": "iH6oqkgBA", "2": "IRc9Yh23bL", "3": "q36HNr8kQ", "4": "Xj9gNx5", "5": "v8kAFwVF", "6": "Igu6Tv"}, "Col_2": {"0": "Gh88JrS", "1": "YYquNGSnYj", "2": "JrRV9", "3": "0kUvmR", "4": "qB0", "5": "IwH41MeCcW", "6": "x9H"}, "Col_3": {"0": "VLIYGO9tW", "1": "J6St", "2": "AWSQ4sZJ8c", "3": "yX5GKdfi... |
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"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"zTWD49CrYE\", \"1\": \"KytC\", \"2\": \"FyDCWu\", \"3\": \"K7w9d7qI\", \"4\":(...TRUNCATED) |
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDA(...TRUNCATED) | "{\"Col_1\": {\"0\": \"Q4do4g\", \"1\": \"\", \"2\": \"gpKlH7iR\", \"3\": \"RlqdM\", \"4\": \"bUU\",(...TRUNCATED) |
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End of preview. Expand in Data Studio
This dataset is generated syhthetically to create tables with following characteristics:
- Empty cell percentage in following range [0,30] (Dense)
- There is clear seperator between rows and columns (Structured).
- 4 <= num rows <= 10, 2 <= num columns <= 6 (Small)
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/small_dense_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
- 176
Size of downloaded dataset files:
4.04 MB
Size of the auto-converted Parquet files:
4.04 MB
Number of rows:
50