I need a complete Automatic Number Plate Recognition solution that accurately reads both car and motorcycle plates during daylight hours. Once a plate is captured, the system must do two things automatically: log the full plate data (image, text, timestamp) into a database and trigger an immediate alert through a simple webhook or REST call. My ideal stack is Python with OpenCV or a comparable vision framework, paired with a modern detection model such as YOLOv8-seg, but I am open to alternatives if you can demonstrate equal or better accuracy. You are free to use an off-the-shelf OCR engine (Tesseract, EasyOCR, PaddleOCR) or a custom-trained model, as long as you meet the accuracy target. Key deliverables • Optimized plate-detection and OCR pipeline tuned for daylight footage • Real-time processing on 1080p video (≥20 FPS on an Nvidia RTX-class GPU or equivalent) • A lightweight dashboard or API endpoint to review stored entries and verify alerts • Complete, well-commented source code with setup instructions and a short calibration guide Acceptance criteria • ≥95 % read accuracy on my supplied daytime test set for both cars and bikes • Logged data must include plate text, full-resolution crop, and UTC timestamp • Alerts must reach my sample webhook within 1 second of detection Provide a brief outline of your planned approach, the libraries you intend to use, and any prior ANPR benchmarks you can share. Once agreed, we can set milestone cuts for model training, integration, and final live test.