Contact person: Cross-Platform .NET MAUI App – Real-Time YOLO Object Detection + OCR on Detected Objects (Android & iOS)

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Location: Managua, Nicaragua

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I need a single .NET MAUI application that runs smoothly on both Android and iOS and combines two core features:

Core Features

1. Real-time YOLO object detection
• Live camera preview with inference running in real time.
• Bounding boxes and labels must be drawn directly on the preview, so users see what the model detects in the moment.
• Ability to capture screenshots of detected objects (with bounding boxes visible) and save them locally.

2. OCR (Text Recognition) on Detected Objects
• After YOLO detects an object, the detected bounding box area should be passed to an OCR model.
• OCR should extract printed text only from inside the detected bounding box (no handwriting/barcodes needed).
• Return extracted text as structured results for further processing.

UI / UX
• Clean, touch-friendly interface.
• Full-screen live camera preview.
• Start/Stop detection toggle.
• Simple results panel showing:
• Detected objects with bounding boxes.
• OCR text extracted from those objects.

Technical Requirements
• Run inference locally (no cloud services).
• Support hardware acceleration where available (GPU, NNAPI, CoreML).
• Modular codebase: easy to swap YOLO/OCR models or upgrade libraries later.
• Must include clear build & run instructions for both Android and iOS.

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Deliverables
• A working .NET MAUI app (Android + iOS).
• Real-time YOLO detection with bounding box overlays.
• Screenshot capture of detections.
• OCR applied specifically on YOLO-detected bounding boxes.
• Extracted text returned alongside detected object data.
• Full source code + documentation.

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Skills Required
• .NET MAUI (cross-platform)
• YOLO model integration (ONNX/TFLite/CoreML)
• OCR model integration (ML Kit, Tesseract, EasyOCR, or CoreML text APIs)
• Android/iOS camera & GPU acceleration
• Strong ML/AI deployment experience

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Budget & Timeline

Open to proposals (please provide Android-only vs Android+iOS estimate).
Delivery target: 3–4 weeks." (client-provided description)