Universe

cinza_pl Computer Vision Dataset

byPlaca CinzaTask:
Object Detection
License:CC BY 4.0388 views15 downloads
See all 8.5k imagescinza_pl letras sample showing 5, 6, 9, H, J, Pcinza_pl letras sample showing 3, 6, 8, 9, H, P, Scinza_pl letras sample showing 1, 4, C, H, Pcinza_pl letras sample showing 2, 7, A, I, J, Ocinza_pl letras sample showing 3, 5, 6, H, J, Xcinza_pl letras sample showing 3, 4, 5, 7, J, V, Xcinza_pl letras sample showing 2, 5, D, H, I, O, Pcinza_pl letras sample showing 2, 4, 6, 7, N, O, Tcinza_pl letras sample showing 4, 8, 9, H, J, O, Pcinza_pl letras sample showing 2, 4, 7, A, F, Ocinza_pl letras sample showing 3, 4, 5, 7, J, V, Xcinza_pl letras sample showing 3, 4, 5, 7, J, V, X

About cinza_pl Dataset

Here are a few use cases for this project:

  1. License Plate Recognition: Use "cinza_pl" to read license plates for monitoring vehicle access, parking management, and traffic control systems. The model can identify characters on license plates in real-time, automating the process and improving efficiency.

  2. Optical Character Recognition (OCR) for Documents: Implement "cinza_pl" in OCR systems to accurately recognize letters and numbers for digitizing printed or handwritten documents. This could help businesses automate data entry and improve accessibility of older documents.

  3. Intelligent Vehicle Navigation: Integrate the "cinza_pl" model into autonomous vehicle systems to help identify road signs and understand street markings. The model would analyze signs with alphanumeric characters for navigation, improving the safety and efficiency of self-driving vehicles.

  4. Inventory Management: Use the "cinza_pl" model to read and identify product labels, tags, and barcodes for inventory tracking purposes. By automating recognition of alphanumeric characters, businesses could improve the speed and accuracy of inventory management.

  5. Handwriting Analysis and Authentication: Apply the "cinza_pl" model to analyze and recognize handwritten characters for various purposes, such as identifying forgery in checks, contracts, or historical documents, and verifying the authenticity of signatures.

Use Free 0, 1 and 2 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, 4"
  },
  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{ cinza_pl_dataset,
  title = { cinza_pl Dataset },
  type = { Open Source Dataset },
  author = { Placa Cinza },
  howpublished = { \url{ https://universe.roboflow.com/placa-cinza/cinza_pl } },
  url = { https://universe.roboflow.com/placa-cinza/cinza_pl },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { oct },
  note = { visited on 2026-07-29 },
}

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