Raspberry Pi Number Plate Recognition FYP: Practical Scope and Demo Plan
Build a realistic number plate recognition FYP with Raspberry Pi, camera capture, OCR workflow, dataset limits, and demo validation.
Rectronx
2026-07-14
Quick Summary
Number plate recognition is possible as a student prototype when the scope is controlled. The goal is to capture a plate image, process it, extract characters, and store the result with confidence notes.
Project Scope
Use Raspberry Pi with a camera in a fixed position. The system captures vehicles or printed plate samples, runs image preprocessing and OCR, then stores detected text in a log.
The project should be presented as a controlled environment prototype, not a law enforcement-grade recognition system.
Suggested Components
Use Raspberry Pi 4 or Raspberry Pi 5, Pi Camera or USB camera, stable lighting, OpenCV, OCR library, local database, and web dashboard. Add an LED or display for status if needed.
Build Steps
- Capture plate images under consistent lighting.
- Crop or detect the plate region.
- Apply grayscale, thresholding, and noise reduction.
- Run OCR and store detected text.
- Test with different angles, fonts, and lighting conditions.
Accuracy and Safety Notes
Recognition accuracy depends heavily on camera angle, lighting, plate style, motion blur, and dataset quality. Use your own test images and avoid collecting private vehicle data without permission.
Common Mistakes
- Testing only one printed plate.
- No failed-case examples.
- Camera mounted too low or too far.
- Treating OCR output as always correct.
- Ignoring privacy in screenshots and reports.