Automatic Number Plate Recognition using OpenCV

The Automatic Number Plate Recognition (ANPR) system is a computer-vision final-year project that automatically detects vehicle registration plates in images and live video, segments the characters and recognizes them with OCR. Built with OpenCV for plate detection and Tesseract/EasyOCR for character recognition, with a Flask web app front end, this number plate recognition project is branded PlateScan and handles Indian plate formats (e.g. MH 12 AB 3456), tilted or skewed plates and low-light frames — exactly the pipeline (detection → segmentation → OCR) that AI/ML viva examiners expect you to explain.

This is a built-to-order project: every deliverable is prepared fresh for you — full source code, report PDF, PPT and viva Q&A — so nothing looks recycled.

1. A frame is captured from an uploaded image, video file or live camera feed.
2. Pre-processing: grayscale conversion, bilateral filtering, CLAHE contrast enhancement and adaptive thresholding.
3. Edge detection (Canny) and contour analysis locate rectangular candidates matching plate aspect ratios; a Haar cascade / contour classifier filters false positives.
4. The plate region is cropped, deskewed with a perspective transform and normalized.
5. Character segmentation splits the plate into individual glyphs via contour bounding boxes, handling merged characters with vertical projection analysis.
6. Tesseract or EasyOCR recognizes each character and returns text with confidence scores.
7. The result is validated against Indian plate patterns, displayed in the PlateScan dashboard and logged with the snapshot.

• Automatic plate localization in images and live webcam/CCTV video streams
• Character segmentation with contour analysis and projection profiles
• OCR recognition via Tesseract and EasyOCR with confidence scoring
• Indian plate format validation (state code, RTO series, registration number)
• Skew/tilt correction using perspective transforms before recognition
• Low-light and night-frame enhancement (CLAHE + adaptive thresholding)
• PlateScan Flask web app: upload image or use live camera, view annotated output
• Detection log with timestamps, snapshots and recognized text export (CSV)
• Batch processing mode for evaluating accuracy over a test image set
• Configurable confidence threshold with manual-review queue for low scores

• Complete, commented Python source code (detection, OCR, web app)
• PlateScan Flask web application with dashboard UI
• Project report PDF (literature survey, methodology, results, accuracy tables, conclusion)
• PPT presentation for final review
• Viva Q&A preparation document (contour theory, OCR internals, OpenCV functions)
• Sample test image/video set + installation and run guide