Video Verification Pipeline
A Python pipeline that turns a screen recording of a verification page into structured, auditable JSON: OpenCV frames, parallel OCR and an optional LLM structuring step.
- Product
- AI document pipeline
- Built with
- Python, OpenCV, Nanonets

inspectable stages, each saved to disk
OCR, one worker per frame
of fields traceable to their source frame
The problem we set out to solve.
It takes one screen recording of someone opening a licence lookup or certificate page and turns it into structured JSON that can be audited. Browser text is tiny in video, so the work is split into small steps instead of asking one model to do everything.
- Browser text in a video is small, compressed and hard to read.
- Verification results must be auditable, not a black-box guess.
- The LLM must never become the source of truth.


Every part, built to work in the real world.
Frame extraction
OpenCV samples the video, by default one frame every three seconds.
Preprocessing
Grayscale, 1.5× upscale, adaptive threshold and sharpening make small text readable.
Parallel OCR
Every frame goes to Nanonets OCR at once, then results are sorted back into order.
Optional structuring
Groq formats the OCR into a strict schema; anything not found stays null.
CLI and API
Runs from the command line or through a FastAPI upload endpoint.
Full audit trail
Frames, raw OCR and structured records are all written to disk.
Every stage writes its result to disk, so a reviewer can trace any field back to the frame it came from. OCR is the source of truth; the model only formats, and it can be switched off in one setting.
- PythonPipeline and CLI
- OpenCVFrames and preprocessing
- Nanonets OCRParallel text extraction
- GroqOptional structuring step
- FastAPIUpload-and-verify API
A reviewable verification workflow for video: every intermediate result is on disk, OCR runs in parallel, and the LLM step is strictly optional.

Prefolk
AI user-testing SaaS
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