Universe

TFT-ID Computer Vision Dataset

byhuyifeiTask:
Object Detection
License:MIT54 views1 download
See all 8.6k imagesTFT-ID table-figure-text sample showing figure, table, textTFT-ID table-figure-text sample showing textTFT-ID table-figure-text sample showing figure, textTFT-ID table-figure-text sample showing figure, table, textTFT-ID table-figure-text sample showing textTFT-ID table-figure-text sample showing figure, textTFT-ID table-figure-text sample showing table, textTFT-ID table-figure-text sample showing figure, textTFT-ID table-figure-text sample showing textTFT-ID table-figure-text sample showing textTFT-ID table-figure-text sample showing textTFT-ID table-figure-text sample showing figure, table, text

About TFT-ID Dataset

8606 STEM academic paper pages and 41,702 objects (table, figure, and text) annotated and verified by myself. You can use this dataset to 100% reproduce TFT-ID-1.0: https://huggingface.co/yifeihu/TFT-ID-1.0

Tables and figures: in the same bounding boxes with their captions. It’s easier to extract captions instead of associating them later. Text: Text sections that belong to the main content (excluding page headers and footers), following left-to-right and top-to-bottom reading order, and can be further converted to text format (title, paragraph, list, code, algorithm, math expression, etc).

Breaking text into smaller sections makes OCR easier. The OCR models won’t have to handle long input/output which could pose serious challenges to both performance and hardware requirements.

The annotations are under MIT licenses. As for the paper images, please follow the original license or user agreement on arXiv (most of them are under CC0 license).

Use Free Table, Text and Figure Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "table, text, figure"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseMIT

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ tft-id_dataset,
  title = { TFT-ID Dataset },
  type = { Open Source Dataset },
  author = { huyifei },
  howpublished = { \url{ https://universe.roboflow.com/huyifei/tft-id } },
  url = { https://universe.roboflow.com/huyifei/tft-id },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2024 },
  month = { sep },
  note = { visited on 2026-07-29 },
}

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