Universe

YoloTA Computer Vision Dataset

byMaskimTask:
Object Detection
License:CC BY 4.061 views3 downloads
See all 1.5k imagesYoloTA character sample showing 9YoloTA character sample showing UYoloTA character sample showing 9YoloTA character sample showing PYoloTA character sample showing HYoloTA character sample showing 9YoloTA character sample showing AYoloTA character sample showing MYoloTA character sample showing 7YoloTA character sample showing 2YoloTA character sample showing UYoloTA character sample showing L

About YoloTA Dataset

Here are a few use cases for this project:

  1. Education Tools: YoloTA can be used to create interactive education tools for both in-classroom and remote learning situations. These tools can analyze student handwriting on a whiteboard to provide immediate feedback on spelling, grammar or mathematical equations.

  2. Document Digitization: The model can be applied to scan and digitize handwritten documents, transforming the analog information into searchable, digital text. This could be particularly useful in archival work or the digitization of historical documents.

  3. Accessibility Solutions: Utilizing this model, it is possible to develop advanced accessibility tools for visually impaired individuals. For instance, a system could interpret handwritten or printed text in real-time and translate it into voice/braille outputs.

  4. Sign Language Recognition: YoloTA's character recognition capabilities could potentially be utilized to interpret and translate sign language for individuals, promoting better communication.

  5. Automated Grading Systems: This AI model can also be used to expedite the process of grading exams or assignments in an education environment, especially when answers are in a multiple-choice or fill-in-the-blank format. The system could automatically recognize the handwritten characters and grade accordingly.

Use Free =, 0 and 1 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": "=, 0, 1, 2, 3"
  },
  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

LicenseCC BY 4.0

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

@misc{ yolota_dataset,
  title = { YoloTA Dataset },
  type = { Open Source Dataset },
  author = { Maskim },
  howpublished = { \url{ https://universe.roboflow.com/maskim/yolota } },
  url = { https://universe.roboflow.com/maskim/yolota },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { jun },
  note = { visited on 2026-07-29 },
}

Similar Projects

See More