Computer Vision Engineer (YOLO + OCR Integration for Live Poker Tables)
Ideally, we are looking for a developer who already has a working or partially ready solution for poker table recognition and can quickly integrate or adapt it to our requirements.
We are looking for a skilled Computer Vision / ML Engineer to integrate ready-made open-source solutions (YOLO + PaddleOCR) into our real-time poker table parser.
Your task:
Take an existing repository such as yolo11-poker-hand-detection-and-analysis or similar, and adapt it to our poker client UI.
The system must detect cards, stacks, pot size, and player actions from a video stream or screen capture in real-time (1โ6 tables simultaneously) and output structured JSON events with minimal latency (<0.5s).
โ๏ธ Responsibilities
- Integrate YOLOv10 / YOLO11 or RT-DETR model for detecting poker elements.
- Use PaddleOCR (custom dictionary: cards, suits, numbers) to extract text (stacks, pot, etc).
- Adapt detection zones and templates to our poker room UI (multi-table layout).
- Implement multi-threaded pipeline for 1โ6 tables in parallel.
- Output standardized JSON events (or WebSocket stream) per detected action.
- Ensure high accuracy and low latency (target: <0.5s delay).
๐งฉ Requirements
- Experience with YOLO (v8โv11), ONNX / TensorRT, PaddleOCR, OpenCV.
- Strong Python or C++ skills.
- Understanding of real-time computer vision, frame capture, and performance optimization.
- Ability to work independently and deliver stable prototypes quickly.
- Experience with gaming/video analytics or OCR tasks will be a plus.
Required languages
Ukrainian | Native |
Published 5 October
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$600-2000
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