DetectOneCard Computer Vision Project
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Here are a few use cases for this project:
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Gaming Platforms: Using the "DetectOneCard" model can enhance online card games by verifying and recognizing cards during a live game. It can automate the process, preventing disputes over what cards are on the table.
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Casino Surveillance: The model can be used in CCTV surveillance software in casinos. It can help track card play, detect cheating, or evaluate dealer's speed and efficiency.
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Inventory and Sorting: The model could be used by manufacturers or retailers that handle large amounts of playing cards. It can streamline inventory processes by recognizing and categorizing different cards automatically.
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Educational Tools: The model can be used to create interactive learning tools or games for both kids and adults. The model can recognize the cards and provide feedback or scores based on the game rules.
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Machine Learning Research: The model could serve as a base structure for researchers who are developing more complex card-recognition or object-recognition algorithms. It can be tested across different card games and setups to study its effectiveness and precision.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
detectonecard_dataset,
title = { DetectOneCard Dataset },
type = { Open Source Dataset },
author = { Academy },
howpublished = { \url{ https://universe.roboflow.com/academy-jh1vy/detectonecard } },
url = { https://universe.roboflow.com/academy-jh1vy/detectonecard },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { aug },
note = { visited on 2024-12-03 },
}