Universe

LPrecognise Computer Vision Dataset

bynew-workspace-awlgrTask:
Object Detection
License:CC BY 4.0170 views9 downloads
See all 2.5k imagesLPrecognise symbols sampleLPrecognise symbols sample showing 0, 4, 5, С, Т, ХLPrecognise symbols sample showing 1, 3, 5, А, В, ОLPrecognise symbols sample showing 1, 6, 9, А, Е, КLPrecognise symbols sampleLPrecognise symbols sample showing 9, А, Т, ХLPrecognise symbols sample showing 1, 2, 3, А, В, КLPrecognise symbols sampleLPrecognise symbols sample showing 2, 3, 7, А, НLPrecognise symbols sample showing 0, 2, 5, А, Н, ОLPrecognise symbols sample showing 6, 9, А, ОLPrecognise symbols sample showing 0, 3, 6, А, В, Т

About LPrecognise Dataset

Here are a few use cases for this project:

  1. License Plate Recognition: This is probably the primary use case for the "LPrecognise" model, given its ability to identify the languages appearing on license plates. In a parking management system, the model can be used to automate entry and exit, payment processing, and violation detection.

  2. Document Verification: Authorities could use this model to scan and verify vehicle registration documents or other documentation that contains these specific symbols.

  3. Traffic Surveillance: Law enforcement agencies can use the model in surveillance cameras to identify and track vehicles based on their license plates for security and law enforcement operations.

  4. Automated Toll Systems: The system could use the LPrecognise model to automatically read license plates and thus process toll payments, reducing the need for manual checks and improving efficiency.

  5. Vehicle Retrieval Systems: In large parking lots or garages, the model could be used for automatic vehicle retrieval. By simply entering the license plate number, the vehicle's location could be provided to the user.

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{ lprecognise_dataset,
  title = { LPrecognise Dataset },
  type = { Open Source Dataset },
  author = { new-workspace-awlgr },
  howpublished = { \url{ https://universe.roboflow.com/new-workspace-awlgr/lprecognise } },
  url = { https://universe.roboflow.com/new-workspace-awlgr/lprecognise },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { may },
  note = { visited on 2026-07-29 },
}

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