Handwriting Analysis — Forensic Signature Authentication
Forensic-grade signature authentication analyzing 200+ signatures against 14 measurable characteristics using OpenCV computer vision and statistical analysis. Identifies autopen patterns, stroke order deviations, and pressure variation anomalies. Multi-AI cross-validation for confidence scoring.
The Problem
- Art forgery epidemic — Forged signatures cost collectors millions annually
- Expert bottleneck — Forensic examiners charge $500–2,000 per analysis with weeks-long backlogs
- Subjective authentication — Expert “feel” is non-reproducible and vulnerable to bias
- Autopen deception — Machine signatures fool untrained eyes but have tells: uniform pressure, zero tremor
14 Measurable Characteristics
- Stroke pressure variation
- Pen lift count and position
- Letter proportions (height-to-width ratios)
- Tremor pattern (natural vs. machine-perfect)
- Slant consistency vs. authenticated exemplars
- Baseline variation
- Stroke speed (inferred from ink pooling)
- Starting point positions
- + 6 additional forensic characteristics
How It Works
- OpenCV preprocessing — Grayscale, adaptive thresholding, morphological operations, contour extraction
- Statistical analysis — Z-score comparison against authenticated exemplar distributions
- Multi-AI cross-validation — Each model provides confidence score and characteristic-level flags
- Confidence output — 0–100 score with per-characteristic breakdown, not a black box
Tech Stack
Python · OpenCV · Statistical Analysis · Multi-AI validation · 200+ signatures · 14-characteristic framework