AI-Powered Forensic Intelligence
Handwriting & Signature Forensics

Uncover Truth
in Every Stroke
& Curve

Advanced AI and machine learning algorithms decode the forensic intelligence embedded in handwriting patterns, stroke dynamics, and signature biometrics — with court-grade precision.

99.4%
Detection Accuracy
6
Analysis Modules
18+
AI Algorithms
<2s
Processing Time
Signature — Live Analysis
Pressure Variance0.87 MPa ±0.02
Stroke Velocity142 mm/s
Pen Lift Events3 detected
Authorship Match94.3% MATCH
Forgery IndexLOW RISK
Handwriting Static Analysis Signature Detection Dynamic Stroke Modeling Forensic Comparison Neural Feature Extraction Biometric Authentication Signature Dynamic Analysis Forgery Detection Court-Grade Reporting AI-Powered Forensics Handwriting Static Analysis Signature Detection Dynamic Stroke Modeling Forensic Comparison Neural Feature Extraction Biometric Authentication Signature Dynamic Analysis Forgery Detection Court-Grade Reporting AI-Powered Forensics
Analysis Modules

Six Forensic
Intelligence Engines

01 — Static · ISO/IEC 17025
Handwriting Static Analysis
A statistically rigorous, court-defensible framework that compares handwriting samples of unknown origin and produces a calibrated Score-Based Likelihood Ratio (SLR) using graph decomposition and a Random Forest ensemble trained on a certified forensic handwriting database.
Random ForestScore-Based LRGraph DecompositionZhang-Suen Thinning
02 — Dynamic · ISO/IEC 19794-7
Handwriting Dynamic Analysis
A concurrent multi-style comparison engine that analyses several handwriting styles simultaneously and ranks them by quantitative similarity score — capturing scribal hand characteristics, style drift over time, and neuromuscular behavioural patterns.
Multi-Style ConcurrentCosine SimilarityLSTM TemporalScribal Hand Model
03 — Detection · mAP50 88.7%
Detect Signature
A Vision Transformer (ViT) fine-tuned with DETR bipartite matching loss — applies global self-attention across the full document to precisely locate handwritten signature bounding boxes. Trained on 2,819 labelled document images from real-world forensic corpora.
YOLOS Vision TransformerDETR BipartiteSelf-AttentionHungarian Algo
04 — Comparison · ENFSI LR
Signature Comparison
Side-by-side forensic comparison via Siamese Neural Network + Dynamic Time Warping, producing a 15-dimension similarity matrix and an ENFSI-compliant Likelihood Ratio output. Three-class output: Genuine, Simulated Forgery, or Random Forgery.
Siamese NetworkDTW AlignmentBayesian LR FusionSHAP Heatmaps
05 — Sig Dynamic · 98.9% Accuracy
Signature Dynamic Analysis
Online signature biometrics capturing signing rhythm, hesitation at inflection points, tremor, stroke ordering, and pen-lift timing via LSTM temporal networks and Hidden Markov Models. Autoencoder-based anti-spoofing blocks replay attacks at 99.8%.
LSTM TemporalHMM SequenceAutoencoder Anti-SpoofSigma-Lognormal
06 — Reports · ISO 17025
Forensic Report Engine
Court-admissible expert reports generated in under 3 seconds — ISO 17025 / SWGDOC / ENFHEX structured, auto-annotated with evidence maps, ENFSI Likelihood Ratio output, and SHA-256 tamper-sealed. Exports to PDF, DOCX, and XML.
ISO 17025 · SWGDOCSHA-256 SealedENFSI LR FormatPDF · DOCX · XML
Explore All Modules →
AI Engine

Powered by Deep Learning
& Forensic Science

Our multi-model AI stack fuses classical forensic science with modern deep learning — delivering explainable, defensible results trusted by examiners, courts, and security teams worldwide.

Convolutional Neural Networks Siamese Networks LSTM / GRU Transformer Attention Dynamic Time Warping Hidden Markov Models Random Forest Bayesian Scoring SHAP / LIME
Explore AI Engine
CoreFlowAI
Static HW
Dynamic HW · Multi-Style
Sig Detection · YOLOS ViT
Sig Comparison · Siamese
Sig Dynamic
Reports
Workflow

From Evidence
to Verdict

01
Ingest
Upload scanned documents, photos, or connect tablet input streams. Supports PDF, TIFF, JPEG, SVG, and live API feeds.
02
Pre-Process
AI preprocessing: noise reduction, binarisation, deskew correction, stroke skeleton extraction, ViT patch sequence embedding, and ImageNet normalisation for transformer-based detection modules.
03
Analyze
Multi-layer AI analysis runs in parallel — deep feature extraction, biometric profiling, temporal modeling, and cross-reference.
04
Report
Expert forensic report generated with confidence scores, annotated findings, visual evidence maps, and full audit trail. Court-ready in <2s.

Every Stroke Tells a Story.
We Decode It.

Used by forensic examiners, financial institutions, legal teams, and law enforcement in 48 countries.

340+
Forensic Labs
2.4M+
Docs Analyzed
48
Countries
99.4%
Accuracy Rate