The following peer-reviewed papers directly underpin the algorithms, methodologies, and standards implemented in ForensicsFlow. All DOIs are verified and link to the published sources.
The largest-scale study to date measuring the accuracy, reproducibility, and repeatability of forensic handwriting comparison decisions by practising forensic document examiners. 86 examiners made 3,713 decisions across samples spanning a range of quality, quantity, and casework attributes. Results establish the empirical error rates that calibrated AI systems must be measured against — the foundation for Score-Based Likelihood Ratio validation in ForensicsFlow.
Introduces the core methodology implemented in ForensicsFlow's Handwriting Static Analysis module: decomposing handwriting into graphical units, assigning them to 40 exemplar clusters, and using the cluster-frequency vector as a feature space for Random Forest comparison. Score-Based Likelihood Ratios are computed from the forest similarity scores against empirical same-writer and different-writer distributions. Funded by NIST/CSAFE cooperative agreement.
Introduces YOLOS — the Vision Transformer (ViT) architecture fine-tuned for object detection using DETR bipartite matching loss, treating the full image as a single patch sequence. YOLOS-Base achieves 42.0 box AP on COCO val with 127M parameters. This paper underpins the Detect Signature module: fine-tuned on forensic document corpora, it achieves mAP50 = 0.887 for handwritten signature bounding box prediction without requiring region proposal networks or non-maximum suppression.
Presents a Spatial Variation-dependent Verification (SVV) scheme using textural features to authenticate handwritten signatures across different writing surfaces and instruments. The study demonstrates that AI-assisted textural analysis reliably discriminates genuine from forged signatures under varying document conditions — informing the preprocessing and feature extraction robustness requirements of the ForensicsFlow static analysis pipeline.
Develops a triplet Siamese network architecture for offline signature verification — extending the standard contrastive Siamese approach with a three-way triplet loss that simultaneously enforces similarity to genuine specimens and dissimilarity from forgeries. The method significantly reduces the Equal Error Rate on standard benchmark corpora and underpins the forgery classification approach in ForensicsFlow's Signature Comparison module.
Provides a systematic benchmark comparison of machine learning approaches for signature verification — including SVM, CNN, Random Forest, and hybrid architectures — evaluated across CEDAR and other public datasets. The study's equal-error-rate analysis across methods informed the multi-algorithm ensemble strategy used in ForensicsFlow, where no single model is relied upon exclusively for the final forensic opinion.
The European Network of Forensic Science Institutes' authoritative best practice manual for forensic handwriting examination — covering examination methodology, evidence evaluation using Likelihood Ratios, reporting standards, and quality assurance requirements. ForensicsFlow's report engine, SLR output format, and verbal scale conform to the guidelines established in this document. All reports generated on the Laboratory and Enterprise plans are structured to satisfy this standard.
Proposes a hybrid CNN-BiLSTM architecture for multi-script offline signature verification that operates without writer-specific enrolment — critical for forensic scenarios where no reference signatures exist. The bidirectional LSTM captures both forward and reverse temporal dependencies in stroke feature sequences. This writer-independent approach directly informs the open-set verification capability of ForensicsFlow's Signature Comparison module.
Every model deployed in ForensicsFlow is trained and validated on publicly available, peer-reviewed forensic datasets — ensuring reproducibility, transparency, and resistance to overfitting on proprietary data.
ForensicsFlow actively engages with academic institutions, government forensic science bodies, and international standards organisations to validate and advance its algorithmic foundations.
The ForensicsFlow platform builds upon and extends open-source forensic software tools developed by academic and research institutions. The following repositories represent the algorithmic foundations implemented in our modules — adapted, extended, and validated for production forensic workflows.
An R package developed by the Center for Statistics and Applications in Forensic Evidence (CSAFE) at Iowa State University. Implements the complete forensic handwriting comparison pipeline: decompose handwriting samples into graph-based graphical units, sort them into 40 exemplar clusters, compare cluster-frequency vectors using a Random Forest ensemble, and output a calibrated Score-Based Likelihood Ratio (SLR) under two competing forensic hypotheses — Hp (same writer) and Hd (different writers).
A web-based handwriting analysis tool built in ASP.NET Core and TypeScript that implements concurrent multi-style scribal hand analysis. Multiple predefined handwriting styles can be analysed simultaneously against a questioned or unknown sample, producing a quantitative similarity score for each reference style and sorting them in ranked order. Designed for manuscript research, palaeography, and historical document attribution — identifying scribes, dating manuscripts, and tracking gradual style evolution over time.
A PyPI-installable Python library developed by the BDALab for the computation of conventionally used handwriting features from online handwriting data. Provides a comprehensive feature extraction pipeline across five categories — kinematic (velocity, acceleration, jerk), dynamic (azimuth, tilt, pressure), spatial (stroke dimensions, intersection counts), temporal (stroke duration, interruptions), and composite (writing tempo, stops, profile change counts). Supports input from SVC, JSON, NumPy, and Pandas. Interfaces with the Featurizer API for scalable feature extraction.
Access the full publication list, request technical documentation, or reach out to discuss research collaboration.