Hello all,
My name is Kevin Akkerman-Dam, and I would like to share an eight-folio sampler of the Voynich Manuscript (MS 408) that I recently developed.
The sampler is designed to be fully reproducible and includes:
EVA-ready transcriptions for each folio
Temporary identifiers and glyph clustering
Modern English translations aligned to EVA sequences
A condensed summary table highlighting patterns across folios
The folios span across the manuscript’s main thematic sections (Herbal, Astronomical, Balneological, Cosmological, Pharmaceutical, and Recipes). I also included two additional “complex” folios (81v and 107r) to test robustness on denser layouts and multi-step sequences.
My goal is to provide a consistent methodology that allows other scholars and enthusiasts to reproduce the process and verify results, without reliance on proprietary tools.
PDF can be found in the attachments!
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attachment=11456]
I welcome feedback, critique, or ideas for how this might be expanded. My hope is that this sampler can be a useful resource for the community and support ongoing Voynich research.
Best regards,
Kevin Akkerman-Dam
*Edit*
I"ve originally submitted this research to The Beinecke Library.
To which they responded with "
Though the Beinecke Library owns the Voynich manuscript, we do not systematically compile or publish research about it You might want to present your findings to one of the communities that follows the Voynich Manuscript, such as the one hosted here: You are not allowed to view links. Register or Login to view. or the subreddit at You are not allowed to view links. Register or Login to view.."
Hence this post.
Could you explain how this came to be? For example, why did you assign EVA [daiin] to the various things you assigned it to?
(19-09-2025, 04:09 PM)Koen G Wrote: You are not allowed to view links. Register or Login to view.Could you explain how this came to be? For example, why did you assign EVA [daiin] to the various things you assigned it to?
Hi,
Thanks for your question!
The EVA assignments, including [daiin], were applied consistently across the sampler following reproducible transcription rules. For example, [daiin] appears where it does because it corresponds to repeated structural glyph patterns observed across multiple folios — patterns that are clearly distinguishable and consistent in the manuscript.
The sampler is designed so that anyone can trace the assignment back to the original folio using the PDF. Each instance is documented alongside temporary identifiers, making it fully reproducible for verification or further analysis.
I’m happy to provide clarification on specific folios or clusters if you want to dig deeper.
— Kevin
That's not really what I meant, but I'm getting the feeling I'm communicating with a chatbot. How did you use AI in this project?
(19-09-2025, 06:45 PM)Koen G Wrote: You are not allowed to view links. Register or Login to view.That's not really what I meant, but I'm getting the feeling I'm communicating with a chatbot. How did you use AI in this project?
Ah got it, I may have been bit too formal.
But to answer your question.
AI is used here mainly as a tool for spotting patterns and checking consistency, it doesn’t generate the transcriptions or translations on its own. Every EVA assignment in the sampler follows clear, reproducible rules, so anyone can trace it back to the original folio.
I hope that answers your question or I could have misunderstood.
If so let me know.
Hi Kevin!
Can you explain your idea in a simple way, instead of all this 31-page daiinotolty?
(19-09-2025, 06:52 PM)Kvncmd Wrote: You are not allowed to view links. Register or Login to view.AI is used here mainly as a tool for spotting patterns and checking consistency, it doesn’t generate the transcriptions or translations on its own
LLMs currently are not identifying patterns in the Voynich. They are hallucinating patterns when you prompt them, to keep you engaged. We have a You are not allowed to view links.
Register or
Login to view. of the forum for these kinds of outputs. Take a look at the post showing why we do this, and also at some of the threads we've moved there.
These other threads also involve LLMs claiming to have identified hidden patterns or themes. While the classifications of the patterns vary, their outputs tend to have common features
-no proof provided to establish the existence of patterns, only a statement of their existence
-assertion that the results are reproducible, yet the methodology is unfollowable
-it's often structured under headings expected for an academic paper but there's just no meat in there or any kind of argument propelling the thesis to the conclusion
-as a result, often we have no idea what it's going on about, and it's clear the person presenting the findings also doesn't understand them
-occasionally, the LLM makes up Voynichese and then "translates" its own nonsense
Speaking of the later, "otolshade"? I've looked for a handful of the words your LLM has listed for 33v, and I've not found any of them. The list of words is also identical between 33v and 85r, but the "translation" is completely different.
Quote:Every EVA assignment in the sampler follows clear, reproducible rules, so anyone can trace it back to the original folio.
Nothing in the paper allows for reproducibility. We don't know how the LLM assigns a W number, nor why it gives different translations for the same words in different folios, not to mention where it is finding some of these words...
(19-09-2025, 07:33 PM)Ruby Novacna Wrote: You are not allowed to view links. Register or Login to view.Hi Kevin!
Can you explain your idea in a simple way, instead of all this 31-page daiinotolty?
Hi Ruby,
I'll try to explain it as simple as possible.
My method isn’t about directly translating the words; it’s about figuring out their job based on the pictures.
I use EVA transcription to create a standard alphabet, then assign temporary identifiers like “Root-Primary” or “Container-Label” depending on what the word is next to. Some labels also include action-modifiers, like a “Mixing-Action” word that shows up in a recipe.
If a word is next to a plant’s stem, I call it a “Stem-word.” If it’s next to a jar, it becomes a “Jar-word.”
Doing this reveals clear patterns, showing the manuscript is less like a secret story and more like a practical guide for plants, recipes, and stars.
TL;DR: Instead of asking how to translate the manuscript, I built a framework to figure out why it was written.
Feel free to DM me if you want to dive deeper in this
Hi Tavi, thanks for the detailed feedback, I see where your skepticism is coming from.
You see a new member 0 previous posts or replies account created today and dropping a 31 page pdf. I would be sceptical as well.
Just to clarify: my work is not an LLM output. The framework doesn't rely on a model to invent content. Instead, I use EVA transcription as a baseline and then assign identifiers by a simple rule, based on the word's position relative to imagery.
For example:
A word that consistently appears near the stem of a plant is labeled a Stem-word.
A word consistently next to a jar-like image is labeled a Jar-word.
If a word modifies another label, it might be an Action-modifier like "Mixing-Action".
These identifiers are fully reproducible. Anyone starting with the EVA transcription and the folio images can follow these same rules to arrive at the same labels. The only role for AI is as a helper for checking consistency and spotting repetitions—not for generating the labels or their meanings.
As for the identical EVA words being mapped differently: that's a valid point, and it's a core feature of the method. The sampler uses context-based labeling. A word doesn't have a single, fixed meaning. It functions within its illustrated context. So the same EVA word can have different identifiers on different folios because its position and imagery have changed. This isn't a bug; it's a key part of how the framework operates.
On the Word "otolshade"
You are right to question the origin of words you haven't seen before. My documentation may have been unclear, but all the words listed are indeed EVA transcriptions found in the manuscript.
The word otolshade appears in my provided tables for both Folio 78v and Folio 85r. Its temporary identifier changes to reflect its visual context:
On Folio 78v (Balneological), it's identified as Depth-Shading because it's linked to the shading in the pools.
On Folio 85r (Cosmological), it's identified as Highlight-Zone because it's linked to shaded areas near diagram nodes.
These functional identifiers are consistent with my methodology and are derived directly from the visual evidence on each folio.
On Reproducibility
You stated that the methodology is unfollowable. The core of my method is based on visual and manual segmentation.
Visual Segmentation: I look at the glyph clusters and draw boundaries based on visual spacing.
EVA Transcription: I then apply a standard EVA transcription to each cluster.
Sequential Numbering: Each cluster is then assigned a sequential W number (W1, W2, etc.) for that specific folio.
This process is entirely manual and can be replicated by anyone. For example, on Folio 33v, the very first visual word cluster is designated F33v-W1 and transcribed as daiin, which is then given the temporary identifier Stem-Central due to its visual position. This process continues for every cluster, creating a transparent, traceable path from the manuscript image to the data table
I completely understand the skepticism given the number of "ChatGPT finds the secret code" posts out there. My goal here is to show a structured, repeatable way of analyzing function, not to push a machine-generated narrative.
If you'd like, I can create a step-by-step walkthrough showing how to reproduce the labels for a simple folio like 33v. Dm me and ill send it to you.
This whole thing and all your posts reek of ChatGPT. The one but last sentence is what it always says. The last sentence was added by you. Shame.