PlateDetect vs Graffiti Guard

PlateDetect is an on-device license plate reading app built for controlled field scans, photo imports, readability checks, local review, and watchlist alerts. Start by adding the plate formats used in your area, such as AA ## or AAA ####. PlateDetect strictly follows the formats you add. If no format is configured, detection stays off. Use Live Scan to read plates with the camera, or import a photo for the same on-device recognition workflow. The app combines plate localization, Apple Vision OCR, configurable confidence thresholds, and duplicate-save timing so History stays focused on useful reads instead of every frame. Field Check is a separate mode for measuring whether a plate is readable from a selected distance. It reports readiness using OCR confidence, plate size, framing, and plate shape without saving the check as a scan. History keeps each saved detection organized with normalized plate text, raw OCR text, detection confidence, OCR confidence, matched format, capture time, source, notes, optional GPS region, and saved media. You can choose how much surrounding image context is kept around the plate crop. Watchlist alerts help identify plates you choose to monitor locally. Standard detections use a quieter confirmation tone, while watchlist matches use a distinct higher alert repeated three times. Plate Rules let you allow or restrict plate endings by day and time window, with a Monday-Sunday overview for quick schedule review. PlateDetect is local-first. Detection runs on device, History is stored locally, Face ID app lock can protect saved records, and backend sync is optional through your own HTTPS endpoint. PlateDetect is a field aid, not a legal identity system. Confirm important reads against official records and follow the rules for your role and location. Standard EULA: https://www.apple.com/legal/internet-services/itunes/dev/stdeula/

Detect likely graffiti in photos and camera captures without uploading images. Graffiti Guard is designed for residents, field teams, councils, and maintenance crews that need a quick, private first review. Its bundled Core ML model runs entirely on your iPhone or iPad and highlights likely graffiti for human verification. The iPad workflow is built for inspections: drop in a photo, adjust confidence, review highlighted regions, then share a referenced summary with the result, confidence, processing time, and recommended next step. Graffiti Guard is designed to shorten the first-review and handoff steps. On-device processing avoids image uploads and inference-server dependency, while consistent references help teams route visible incidents. Actual time or cost savings depend on each organisation's workflow and should be measured in a pilot. Why it exists Recurring unwanted graffiti creates cleanup pressure and surface damage in Melbourne and cities worldwide, including Paris, Brussels, Bucharest, and Detroit. Graffiti Guard helps teams review visible incidents earlier and coordinate maintenance for cleaner, better-kept public spaces. Key features: - Private, on-device image processing - No inference server, account, or internet connection required - Camera, Photos, Files, and iPad drag-and-drop support - Adjustable confidence threshold - Shareable inspection summaries with clear next steps - Universal iPhone and iPad interface Created by Pierre-Henry Soria (PierreHenry.dev), an AI software engineer and consultant specialising in computer vision, on-device machine learning, privacy-conscious systems, and production AI deployment. Graffiti Guard is the mobile companion to his open-source Graffiti Detection AI Model Python library. Graffiti Guard detects likely graffiti in images. It does not predict future vandalism and should not replace human review.