20-11-2025, 09:39 AM
(This post was last modified: 20-11-2025, 12:48 PM by quimqu.
Edit Reason: Added Stolfi's suggestion
)
Yesterday Rafal asked me to position the Voynich. I told him that I thought it was not a good idea to position the Voynich in the Penteteuch study, but after re-thinking it... well, we don't know the text fo the Voynich, so it is the same comparing it with the Pentateuch or with other books... And so I put the Voynich data into the PCA plot:
We can see that the Voynich (as a whole) comes out close to Latin in the PCA plot, but Voynich A and Voynich B behave quite differently from each other. The thing is that PCA can be a bit tricky here, because what it shows is mainly the directions where the data varies the most. It is a plot of variation, not a plot of real similarity.
So in those high-variation dimensions, the Voynich ends up near Latin. But that doesn’t mean they are actually close when you look at the underlying metrics. And when we focus on the KPIs where the Voynich has smaller real distances… the results are actually quite surprising:
If we calclate the euclidean distance of the KPI, in all the Voynich corpus (EVA, CUVA, A and B) the nearest language is Mandarin, followed by English.
But how near it is from Mandarin? In fact, they are not near at all. Graph metrics show that, although Voynich may be closer to Mandarin than other languages, it really doesn't resemble it at all. In fact, the differences are very large. The word occurrence patterns of the Voynich text do not resemble the word occurrence patterns of those translations of the Pentateuch.
Voynich graphs have longer paths, so, less internal connectivity and more fragmentation. Natural languages, such as Mandarin, show more compact and well-connected networks.
In natural languages, contexts tend to cluster into communities and repeated patterns, which is not the case in Voynich.
Degree preservation (sigma_degpres): This indicator is much higher in the MS than in any natural language in the Pentateuch corpus. This means that the distribution of bigrams and co-occurrences is much more rigid and less compatible with the variability of any real language.
In Voynich, nodes with similar patterns have a greater tendency to connect to each other, while in natural languages this tendency is very weak or non-existent. So, similar words tend to link together, but in natural languages this does not happen.
Voynich shows a much higher diversity of forms than in human languages, especially in the EVA A variant. This (high TTR) is incompatible with natural texts, which have much more marked repetitions and structures. The MS uses lots of different words but much less repeated than any natural language.
Zipf distribution: although it follows an approximately Zipfian behavior, the slope is somewhat steeper than in natural languages. Therefore, a more regular and more controlled frequency distribution, which also does not fit with the variability of any real language. This means that the most frequent words are much ore frequent than in a natural language and that the less frequent words are much les frequent also.
A final note:
In this study we are comparing graph based KPIs extracted from the Pentateuch in several languages with the same KPIs extracted from the Voynich text. Since the content of the Voynich manuscript is unknown, part of the differences observed in the graph metrics could be influenced by the underlying subject matter or internal structure of the text. In other words, some patterns may reflect the nature of the content itself rather than a linguistic property. For this reason, these results should be interpreted with caution. The comparison shows structural differences, but it does not imply that the Voynich behaves like or unlike any specific language in terms of meaning or topic.
We can see that the Voynich (as a whole) comes out close to Latin in the PCA plot, but Voynich A and Voynich B behave quite differently from each other. The thing is that PCA can be a bit tricky here, because what it shows is mainly the directions where the data varies the most. It is a plot of variation, not a plot of real similarity.
So in those high-variation dimensions, the Voynich ends up near Latin. But that doesn’t mean they are actually close when you look at the underlying metrics. And when we focus on the KPIs where the Voynich has smaller real distances… the results are actually quite surprising:
If we calclate the euclidean distance of the KPI, in all the Voynich corpus (EVA, CUVA, A and B) the nearest language is Mandarin, followed by English.
| Voynich variant | Language 1 | Language 2 | Language 3 | Language 4 | Language 5 | Language 6 | Language 7 | Language 8 | Language 9 | Language 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| Voynich CUVA | Mandarin Union (15516.18) | English (17416.91) | Mandarin Other (20211.07) | Vietnamese (36435.13) | Spanish (63245.70) | French (67399.46) | German (74894.93) | Hebrew (125298.75) | Russian (126951.13) | Latin (140980.96) |
| Voynich EVA | Mandarin Union (14941.14) | English (16929.77) | Mandarin Other (19719.66) | Vietnamese (36023.59) | Spanish (63012.04) | French (67169.80) | German (74703.50) | Hebrew (125042.78) | Russian (126656.67) | Latin (140810.12) |
| Voynich EVA A | Mandarin Union (102035.97) | English (106230.68) | Mandarin Other (107906.38) | Vietnamese (125713.50) | Spanish (153639.03) | French (155921.74) | German (165153.76) | Hebrew (215671.55) | Russian (217425.71) | Latin (230164.48) |
| Voynich EVA B | Mandarin Union (51741.69) | English (55704.18) | Mandarin Other (57564.85) | Vietnamese (75215.28) | Spanish (102978.62) | French (107093.42) | German (114510.12) | Hebrew (165138.20) | Russian (166790.26) | Latin (180836.49) |
But how near it is from Mandarin? In fact, they are not near at all. Graph metrics show that, although Voynich may be closer to Mandarin than other languages, it really doesn't resemble it at all. In fact, the differences are very large. The word occurrence patterns of the Voynich text do not resemble the word occurrence patterns of those translations of the Pentateuch.
Voynich graphs have longer paths, so, less internal connectivity and more fragmentation. Natural languages, such as Mandarin, show more compact and well-connected networks.
In natural languages, contexts tend to cluster into communities and repeated patterns, which is not the case in Voynich.
Degree preservation (sigma_degpres): This indicator is much higher in the MS than in any natural language in the Pentateuch corpus. This means that the distribution of bigrams and co-occurrences is much more rigid and less compatible with the variability of any real language.
In Voynich, nodes with similar patterns have a greater tendency to connect to each other, while in natural languages this tendency is very weak or non-existent. So, similar words tend to link together, but in natural languages this does not happen.
Voynich shows a much higher diversity of forms than in human languages, especially in the EVA A variant. This (high TTR) is incompatible with natural texts, which have much more marked repetitions and structures. The MS uses lots of different words but much less repeated than any natural language.
Zipf distribution: although it follows an approximately Zipfian behavior, the slope is somewhat steeper than in natural languages. Therefore, a more regular and more controlled frequency distribution, which also does not fit with the variability of any real language. This means that the most frequent words are much ore frequent than in a natural language and that the less frequent words are much les frequent also.
A final note:
In this study we are comparing graph based KPIs extracted from the Pentateuch in several languages with the same KPIs extracted from the Voynich text. Since the content of the Voynich manuscript is unknown, part of the differences observed in the graph metrics could be influenced by the underlying subject matter or internal structure of the text. In other words, some patterns may reflect the nature of the content itself rather than a linguistic property. For this reason, these results should be interpreted with caution. The comparison shows structural differences, but it does not imply that the Voynich behaves like or unlike any specific language in terms of meaning or topic.
![[+]](https://www.voynich.ninja/images/collapse_collapsed.png)