Structural Analysis of the Voynich Manuscript — Elucidating a Knowledge System through Statistical Methods

We present a computational statistical analysis of the Voynich Manuscript (VMS). The text is shown to be neither random nor a simple cipher, but an intentionally designed information-encoding system (Pareto-optimal transition table, Zipf's law R²≥0.94, Poissonian burstiness B≈0). We identify a four-layer descriptive system (character, morpheme, syntax, scribal dialect) and a pharmaceutical grammar including the sh→ch material-transformation process, a triangular processing cycle (edy→eedy→ain), a Dioscoridean five-attribute system (ct-prefix), and an ok→ch universal grammar (53–78% across all sections). The generative model achieves dist=0.0143. Structural-level reading was completed for 90.3% of all words; content-level decipherment remains unachieved.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-17
DOI
https://doi.org/10.5281/zenodo.19058611
Primary Topic
Intelligence, Security, War Strategy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Structural Analysis of the Voynich Manuscript — Elucidating a Knowledge System through Statistical Methods

Mitsuro Matsuta
Zenodo (CERN European Organization for Nuclear Research)
Intelligence, Security, War Strategy
article

Structural Analysis of the Voynich Manuscript — Elucidating a Knowledge System through Statistical Methods

Mitsuro Matsuta
article en

Abstract

We present a computational statistical analysis of the Voynich Manuscript (VMS). The text is shown to be neither random nor a simple cipher, but an intentionally designed information-encoding system (Pareto-optimal transition table, Zipf's law R²≥0.94, Poissonian burstiness B≈0). We identify a four-layer descriptive system (character, morpheme, syntax, scribal dialect) and a pharmaceutical grammar including the sh→ch material-transformation process, a triangular processing cycle (edy→eedy→ain), a Dioscoridean five-attribute system (ct-prefix), and an ok→ch universal grammar (53–78% across all sections). The generative model achieves dist=0.0143. Structural-level reading was completed for 90.3% of all words; content-level decipherment remains unachieved.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 20%
Intelligence, Security, War Strategy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.