The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SciTSR-Logical: A Line-Level OCR Conversion of the SciTSR Dataset
Dataset Description
This dataset is a converted and enhanced version of the SciTSR (Scientific Table Structure Recognition) dataset, reformatted for line-level OCR and Table Structure Recognition (TSR) tasks.
While SciTSR provides excellent logical structure information, this version focuses on creating a direct link between low-level OCR output and that structure. For each table, the dataset includes:
- A high-resolution cropped PNG image of the table (rendered at 144 DPI).
- A detailed JSON file that maps each detected text line's physical bounding box to its logical grid coordinates (
[row_start, row_end, col_start, col_end]).
This format is ideal for training and evaluating Document AI models that perform OCR and TSR in a unified manner.
How to Use
You can load an example by pairing the images from the cropped_images directory with the JSON annotations in the logical_gt directory.
import json
from PIL import Image
from pathlib import Path
# Assume dataset is loaded or cloned locally
base_path = Path("./") # Path to the dataset directory
# Get a list of all examples
gt_files = list((base_path / "logical_gt").glob("*.json"))
example_file = gt_files[0]
# Load the annotation data
with open(example_file, 'r') as f:
annotations = json.load(f)
# Load the corresponding image
image_path = base_path / "cropped_images" / (example_file.stem + ".png")
image = Image.open(image_path)
# Display the first annotation
first_line = annotations[0]
print(f"Text: {first_line['text']}")
print(f"Bounding Box: {first_line['box']}")
print(f"Logical Coordinates: {first_line['logical_coords']}")
# image.show()
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