A statistically rigorous, court-defensible forensic framework that compares two handwriting samples of unknown origin and produces a calibrated Score-Based Likelihood Ratio (SLR) — quantifying the strength of evidence for or against common authorship under the two competing forensic hypotheses.
The static analysis engine processes handwriting images through a multi-stage pipeline: image preprocessing and thinning reduce each sample to its skeletal graph structure; feature extraction decomposes this graph into node-level descriptors; and a trained Random Forest classifier combines these features into a single similarity score. This score is then evaluated against a large reference population to produce a calibrated Score-Based Likelihood Ratio — a probabilistically grounded forensic opinion consistent with ENFSI and OSAC best-practice guidelines.
A concurrent multi-style comparison engine that analyses several handwriting styles simultaneously, ranks them by quantitative similarity to a questioned sample, and produces an interpretable similarity score for each predefined style — enabling rapid discrimination across large reference populations.
Dynamic analysis captures the habitual behavioural and morphological characteristics of a writer's style by decomposing each sample into a high-dimensional feature vector. When a query (questioned) sample is submitted, the engine computes a similarity score against every enrolled reference style in the database simultaneously. Styles are then sorted in descending order of similarity, giving the examiner an immediate ranked list with associated confidence scores and a visual comparative summary — dramatically accelerating the triage phase of questioned document examination.
A Vision Transformer–based object detection engine fine-tuned specifically for handwritten signature detection in scanned documents. It locates every signature region precisely within complex, multi-page documents — regardless of scan quality, overlapping stamps, form fields, or mixed content — and returns bounding box coordinates with associated confidence scores.
The detection engine ingests an input document image and feeds it through the Vision Transformer backbone as a sequence of fixed-size non-overlapping patches. A set of learnable detection tokens are appended to the patch sequence and processed through multiple transformer encoder layers — each attending to every other patch globally. The DETR bipartite matching loss ensures that each detection token specialises in predicting exactly one signature bounding box, with the model simultaneously learning where signatures appear and what they look like across diverse document types, handwriting styles, and scan conditions.
Forensic side-by-side analysis between questioned and reference signatures. A Siamese neural network computes a scored similarity matrix across 15 feature dimensions with a 99.4% forgery detection rate.
The Signature Comparison module is the forensic examiner's most powerful tool — digitized and scaled. It produces not just a similarity score, but a complete forensic opinion backed by 15 quantified feature dimensions, visual evidence maps, and Bayesian likelihood ratios formatted for expert witness testimony.
| Mode | Description | Min Specimens | Use Case |
|---|---|---|---|
| 1:1 | One questioned vs. one reference | 5 reference | Single document dispute |
| 1:N | One questioned vs. specimen library | 5 per subject | Unknown author identification |
| N:N | Batch cross-comparison matrix | 3 per subject | Large-scale fraud investigation |
Online signature biometrics capturing the complete temporal execution — rhythm, hesitation, tremor, stroke order, and pen-lift frequency. Forgery detection invisible to the naked eye, in under 500ms.
A skilled forger can replicate the visual appearance of a signature — but they cannot replicate the behavioral execution. Signing rhythm, micro-hesitations at inflection points, pressure transitions, and pen-lift timing are deeply ingrained neuromuscular habits that remain stable over a person's lifetime. This module captures and quantifies them, making it the highest-security signature verification system available.
Court-admissible expert reports generated in under 3 seconds — annotated, structured, and formatted to ISO 17025, SWGDOC, and ENFHEX standards. From raw analysis to expert opinion in one click.
Forensic conclusions are only as useful as the documents that communicate them. The Report Engine automatically assembles comprehensive expert witness reports from any combination of module outputs — complete with annotated evidence panels, statistical confidence tables, methodology references, and formatted examiner commentary sections ready for submission to courts, arbitration panels, or internal compliance teams.
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