Research and evaluation

Built through testing, not assumptions.

VisoraAI’s evaluation plan connects image conditions to recognition behavior. The repository does not yet contain a verified results dataset, so this page documents the method and the evidence still required instead of presenting invented performance figures.

01

What is being tested

Controlled printed samples can be captured across motion blur, glare, distance, framing, and lighting conditions. Single-frame and burst-stacked candidates should be evaluated against the same ground truth.

  • Character Error Rate
  • Recognition acceptance or rejection
  • Burst depth
  • Processing time
  • Blur and glare severity
02

Why these measures matter

A recognition score alone does not explain whether the complete reading interaction works. Latency, rejection behavior, and guidance quality matter because the system must help the user recover when the first image is poor.

03

Current evidence status

Verified dataset results have not been located in this repository. Charts remain deliberately qualitative until the sample set, ground truth, test conditions, and code version are documented together.

Controlled evaluation in progress
04

Limitations to record

A useful report should state camera hardware, document types, font sizes, lighting setup, blur generation method, sample count, and the difference between model confidence and correctness.

05

What changes after testing

Evaluation should inform blur thresholds, burst depth, preprocessing profiles, confidence gates, and guidance priorities. Every change needs a traceable reason rather than a decorative metric.