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metadata
task_categories:
  - object-detection
tags:
  - roboflow
  - roboflow2huggingface
Hoixi/TR-FinTable-Dataset-v4

Dataset Labels

['table', 'table column', 'table row', 'table spanning cell']

Number of Images

{'valid': 6, 'train': 14}

How to Use

pip install datasets
  • Load the dataset:
from datasets import load_dataset

ds = load_dataset("Hoixi/TR-FinTable-Dataset-v4", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/tables-cfun2/pdf-exporter/dataset/7

Citation

@misc{
                            pdf-exporter_dataset,
                            title = { PDF Exporter Dataset },
                            type = { Open Source Dataset },
                            author = { Tables },
                            howpublished = { \\url{ https://universe.roboflow.com/tables-cfun2/pdf-exporter } },
                            url = { https://universe.roboflow.com/tables-cfun2/pdf-exporter },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2025 },
                            month = { apr },
                            note = { visited on 2025-04-14 },
                            }

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on April 14, 2025 at 9:37 PM GMT

Roboflow is an end-to-end computer vision platform that helps you

  • collaborate with your team on computer vision projects
  • collect & organize images
  • understand and search unstructured image data
  • annotate, and create datasets
  • export, train, and deploy computer vision models
  • use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 20 images. Tables are annotated in COCO format.

The following pre-processing was applied to each image:

  • Resize to 640x640 (Stretch)

No image augmentation techniques were applied.