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Posted by: extent_of_foxes - Yesterday, 01:38 AM - Forum: Analysis of the text
- Replies (2)
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How's this for a method of creating a text with oddly low entropy?
- Start with a plaintext in a language written in an alphabet.
- Take each word and put the letters in some fixed order, possibly alphabetical, possibly some other order.
- If there are now clusters of duplicates of the same letter, maybe remove duplicates, or maybe slightly change the order to move them apart.
- Remove the spaces between the words, and insert new spaces wherever you like.
- Replace the letters with symbols, using a simple substitution cipher. Small shifts are allowed for aesthetic reasons, such as to move "c" before a gallows to create a benched gallows.
Based on a not-terribly-reproducible analysis of the first 10k EVA-letters of the manuscript, I came up with "qptkfscheoldaginmry" as a guess at the order of the letters. I then manually read through the first 10k EVA-letters and inserted spaces where the letters "jumped back in the order", that is, at the supposed breaks between words in the plaintext. This was surprisingly subjective. For example, my rules allow "qotor" to be a single word containing two of whichever letter maps to EVA-o, with one of them moved before the "t" to avoid the double "o". But it also seems that "qo" is a common two-letter word. So should "qotor" be split into "qo" and "tor"?
The most common apparent two-letter words are (anagrams of): qo ty ky ch ol dy sy sh or da so.
The most common three-letter words are (anagrams of): cho chy sho shy dar tor kol tol kor tey she ody car cha.
The most common four-letter words are (anagrams of): chor tchy chol kchy shol shor dain chey shey char keey pchy.
I notice some patterns among the common (anagrams of) words: cho sho chy shy, chor chol shor shol, tor kor tol kol. It's certainly possible that a real language could have words like this, especially if the patterns are not as strict as they appear to be, as an artifact of the sorted anagram process. It's still striking, though.
There are some words that just contain several EVA-e: ee, eee, eeee. Roman numerals, with "iiii" instead of "iv" for some reason?
I attempted to match the common words here with (anagrams of) common words in Latin, Italian (modern; I couldn't find historical word frequencies), and a very small corpus of historical French. Sometimes it seemed promising for a while but eventually none of my attempts worked out.
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| Question about unicity distance |
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Posted by: kckluge - 21-05-2025, 11:54 PM - Forum: Analysis of the text
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I could swear there was a reference to unicity distance in a recent comment, and I was going to ask this there, but for the life of me I can't find it -- so here we go...
Unicity distance "is the length of an original ciphertext needed to break the cipher by reducing the number of possible spurious keys to zero in a brute force attack" (You are not allowed to view links. Register or Login to view.). It's a potential way of trying to fomalize the "too many degrees of freedom" critique of proposed Voynich solutions as well as other possible prunings (i.e., if you can't get a coherent stretch of plaintext from a 28+ character long stretch of the ciphertext you haven't proposed a credible solution of the text as a simple monoalphabetic cipher).
What I can't quite figure out is how to correctly compute the unicity distance for something like Brumbaugh's proposed cipher. To recap for those unfamiliar with his "solution", encipherment proceeded in two stages:
1) Convert from plaintext letters to digits using the following grid:
a, j, v = 1
b, k, r = 2
c, l, w = 3
d, m, s = 4
e, n, x = 5
f, o, t = 6
g, p, y = 7
h, q, u = 8
i, -us, z = 9
2) replace each digit with one of several glyphs corresponding to that digit
So, for instance, his hypothetical enciperhing of one of the labels near the upper right corner of You are not allowed to view links. Register or Login to view. is
P E P P E R --> 7 5 7 7 5 2 --> (EVA) s a r ch a r
so each of the three instances of the digit 7 gets replaced by a different Voynich script glyph ('s', 'r', and 'ch'), while both 5's get replaced by EVA 'a' (for the purposes of this discussion ignore that EVA 'r' also maps to the digit 2 here -- that has to do with how Brunbaugh sees variant forms that get grouped together as 'r' in EVA...)
Is there someone out there who knows how to compute the unicity distance for a cipher like this, and if so could you walk me through it?
Thanks,
Karl
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| Historical ciphers, when they were introduced and their effect |
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Posted by: Koen G - 20-05-2025, 02:13 PM - Forum: Analysis of the text
- Replies (23)
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After having read the paper by Marco Vito that was highlighted by Mark here: You are not allowed to view links. Register or Login to view. , I feel like we need a to the point overview of which ciphers were used in the 15th century, when they were introduced and what their effects would be. Could they make a plaintext more like Voynichese, or do they have an opposite effect?
Your input, additions and corrections will be very much required and appreciated. If this turns into something decent, I can make an easy to find version of it in the curated subforum. This might be helpful for newcomers.
1) Simple substitution. - Each letter of the plaintext consistently corresponds to a symbol of the ciphertext. (Example: Caesar cipher).
- Existed before the 15th century.
- Popular with solvers, but there is a broad consensus among scholars that the Voynich text is not the result of a simple substitution cipher.
2) Introduce nulls.- Some extra characters in the ciphertext don't correspond to anything. This can be used to obscure the most frequent characters, break up common bigrams etc.
- Existed before the 15th century (?)
- Effects: larger glyph set, increased entropy (sometimes or always?)
--> even LESS like Voynichese
3) Homophonic cipher- Each plaintext letter can be replaced by multiple ciphertext symbols. Hides frequency -> harder to crack
- Existed before the 15th century (?)
- Effects: larger glyph set; I would expect higher entropy but I'm not sure.
--> even LESS like Voynichese
4) Polyalphabetic, "Alberti" cipher- Allows the encoder to shift to a different substitution alphabet within the same cipher.
- Leon Battista Alberti, 1466 (-> significantly later than Voynich MS). Existed earlier e.g. in Arab sources.
- Effects: glyph set may remain the same; any textual patterns are obscured.
--> very distant from the structured and rigid Voynichese.
5) Steganography- Any method where the actual information is hidden within less suspicious data. Hard to rule out: the only limit is your imagination. Note: the Voynich manuscript appears encoded or "secret" throughout, so it would be an edge case of steganography to begin with if any actual info is concealed in what already looks like a ciphertext.
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| Histocrypt 2025 |
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Posted by: ReneZ - 20-05-2025, 11:19 AM - Forum: News
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This year's Histocrypt conference is held In Poznan, Poland, from 16 to 18 June.
Histocrypt (unusually) publishes its conference proceedings already before the event, and they are available here:
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| Special "nymphs" around Gemini |
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Posted by: Koen G - 19-05-2025, 07:40 PM - Forum: Imagery
- Replies (3)
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This is from a discussion in another You are not allowed to view links. Register or Login to view., where Juan Sali linked to the four temperaments in You are not allowed to view links. Register or Login to view., 15th century You are not allowed to view links. Register or Login to view. 26r.
The fact that these four figures are standing on four elements reminded me of something that's always fascinated me about the figures around the "Gemini" pair. There is so much variation in them. Just think of the "lustful man" behind the woman in a "seductive" pose. The other woman stretching both arms horizontally, rotating in part toward a rare frontal perspective. The young girl in a full, dotted dress.
But there's also four figures standing on something. And I remembered one of these things is grass. The grass one is separate, the other three are in a row. These are also all different: one nude figure is standing on a surface with some pattern, which is surrounded by a band with another pattern. One apparently young man is standing on something with two "scallops" on one end and a circle with four lines on the other end. The final one is standing on a similar thing but it does not have these additional features.
The following is NOT a proposed identification, I am just putting the figures together for comparison.
I don't think there is all that much to help us out here, apart from one thing. The young man, who may correspond to the "airy" Sanguine, has something going on with his arms/hands. Especially the hand that is lowered - might that be a bad attempt at a leather glove? Obviously gloves are worn for falconry, one of the Sanguine's attributes.
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| Upcoming Voynich program at the Getty |
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Posted by: LisaFaginDavis - 19-05-2025, 01:55 PM - Forum: News
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Hi, everyone,
Sorry for my radio silence - my day job has kept me very busy (it isn't easy working in the humanities in the United States at the moment). I'm also working on three different interdisciplinary Voynich collaborations, implementing various methodologies to try and determine the original sequence of bifolia within the different sections of the manuscript.
I wanted to let you all know that I will be participating in an online program about the VMS sponsored by the Getty Museum in Los Angeles and hosted by curator Elizabeth Morrison, taking place on June 13 at 3 PM Eastern. I hope you will join us! Click here for more information and to register:
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- Lisa
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| An attempt at extracting grammar from vord order statistics. |
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Posted by: davidd - 19-05-2025, 03:55 AM - Forum: Analysis of the text
- Replies (25)
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Hi All,
Based on vords coming before and after, grouping vords into vordgroups looks possible.
The past few weeks i have been programming in python to do some analysis and statistical informed guessing on grammar in voynechese. I am happy to announce to you these partial/preliminary results. There seem to be statistically significant groupings of vords that have either increased or decreased likelyhood to either precede or follow certain other groupings of vords.
What i am looking for
I would like to make some academic paper out of these results. That is why i am looking for the help of any academic voynich researcher that would like to collaborate. These results look statistically significant to my amateur eyes but I still have to do some p-value calculations, I think Chi Square would be the appropiate one for this.
assumptions
+ A and B are different languages.
+ each vord is matching a word in a real language
+ the real language has some form of positional grammar, ex like some prepositions come with a genetive case associated directly following the preposition
+ vords have only one meaning and every time a vord is used it means only that one meaning (statistics will still work even if this one isnt true)
method:
language A and language B are processed seperately
for each vord besides frequency also tally preceding and following vords, respecting paragraphs and ignoring line breaks
for all vords that appear at least 4 times, put them in little vordgroups up to around 5 vords each based on similarity in vords coming before and after them.
now score all vordgroups against eachother and merge the most similar ones, not looking at each individual vord transition but transitions from vordgroup to vordgroup
merge until desired amount of vordgroups left
score each of the most frequent vords against all groups to see if any would fit better in another group.
safeguards:
By just looking at the more frequent vords there is less wiggle room than when assigning unique vords to some group to increase the score.
Because the non frequent vords were counted in the total for the percentage calculation, this makes total transition frequency to each of the labeled groups lower.
cons/doubts/possible improvements:
The algoritm I built is made to find these patterns. It has not been tested on random noise or other language samples.
It takes a lot of time, the merging step takes aroung one hour on my poor old pc.
there may be some bugs in the searching algorithm, It is not very stable, the groups that come out are different every time. Probably some memory in python that gives a different order every time. Maybe it is an omen that the method may be flawed.
The output is very long, but with analysing over 27000 vords that is somewhat inevitable. A big part of the output is guessing for all the non-frequent vords in which group they would fit best.
It really feels great to be standing on the shoulders of giants and looking further than anyone before.
Thanks to the members of the voynich ninja and the maintainers of websites about voynich.
Maybe this work can help provide a break through for somebody else.
the results:
statistics about language A and language B are in the same file. first all A output, than all B output
line number chapter
1 Language A
13 initial groups
99 merging
380 vordgroup stats
847 transition tables
894 moving vords to other groups
1217 guessing but not adding to groups of other vords
4466 vordgroup stats
5024 transition tables
5079 Language B
5087 initial groups
5233 merging
5775 vordgroup stats
6149 transition tables
6197 moving vords to other groups
6850 guessing but not adding to groups of other vords
11434 vordgroup stats
11930 transition tables
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Code: vordgroup 56: cheody 56 446
members: ['cheody', 'opar', 'shek', 'sheody', 'shody', 'she', 'opchedy', 'psheody', 'opchdy', 'cheky', 'tchy', 'chekaiin', 'ytedy', 'olkchedy', 'ytchey', 'ytody', 'cholky', 'chcphy', 'lkeey', 'ycheedy', 'shor', 'olky', 'sshey', 'shckhey', 'keol', 'teeody', 'shaiin', 'lkeeedy', 'ycheeo', 'cheoty', 'shekeey', 'chotal']
num members: 32
vord count: 446
groupname: cheody
lesser likely following : chedy 5.16% instead of 16.02%
more likely following : daiin 6.95% instead of 3.37%
lesser likely followed by : chedy 8.07% instead of 16.02%
more likely followed by : qokain 21.08% instead of 15.88%
coming from group <groupname> followed by <groupname> which has a relative size of <x>
---------------------------------------------------------
< other> -> 31.39% <cheody> 37.44% -> < other>
< chedy> -> 5.16% <cheody> 8.07% -> < chedy> rel size: 16.02%
< ol> -> 6.50% <cheody> 6.50% -> < ol> rel size: 12.64%
< aiin> -> 11.66% <cheody> 3.14% -> < aiin> rel size: 10.69%
< daiin> -> 6.95% <cheody> 3.59% -> < daiin> rel size: 3.37%
< qokain> -> 14.13% <cheody> 21.08% -> < qokain> rel size: 15.88%
< dar> -> 2.02% <cheody> 2.69% -> < dar> rel size: 2.14%
< okaiin> -> 0.90% <cheody> 0.90% -> < okaiin> rel size: 0.72%
< okain> -> 0.90% <cheody> 0.45% -> < okain> rel size: 0.78%
< okeey> -> 0.67% <cheody> 0.22% -> < okeey> rel size: 0.52%
< otar> -> 0.67% <cheody> 0.22% -> < otar> rel size: 0.58%
< otaiin> -> 1.57% <cheody> 3.36% -> < otaiin> rel size: 1.28%
< o> -> 6.73% <cheody> 3.36% -> < o> rel size: 4.40%
< oty> -> 2.02% <cheody> 1.35% -> < oty> rel size: 1.59%
< shol> -> 1.35% <cheody> 1.35% -> < shol> rel size: 0.81%
< am> -> 2.02% <cheody> 2.91% -> < am> rel size: 1.92%
< cheody> -> 2.24% <cheody> 2.24% -> < cheody> rel size: 1.90%
< chedaiin> -> 0.00% <cheody> 0.00% -> < chedaiin> rel size: 0.36%
< yteedy> -> 2.47% <cheody> 0.45% -> < yteedy> rel size: 0.53%
=========================================================
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