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| Help with Arabic/Syriac Reading of F1R |
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Posted by: 008348dc760f858fd668476b75fb6f - 05-04-2026, 09:34 AM - Forum: Theories & Solutions
- Replies (5)
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The following is a call for help based on a reading of You are not allowed to view links. Register or Login to view. and the manuscript as Arabic (with Syriac inflections). The glyph-to-phoneme mapping was produced by cross-referencing Maimonides' Medical Glossary and Paul Sbath's translation of the Kitāb al-Azmina (Book of Times) by Ibn Māsawayh against the MS. A majority of findings came from treating the manuscript's sections as transformative "gates", mapping token transitions and noting Galenic quality changes.
This framework suggests the manuscript was written by an East Syriac (Church of the East) physician-monk from the Mosul–Alqosh corridor, c. 1400–1440, using a purpose-built phonemic cipher to encode Arabic pharmaceutical content within a Syriac liturgical framework. My assumption is that the folio is a colophon, an introductory text for the MS. Its token structure is different to anything in the other sections.
I have rendered You are not allowed to view links. Register or Login to view. into phonemes. If anyone here can lend an eye and ear and sound out the phonemes, I would appreciate a "reading" and interpretation if there is one to be had. Specifically:
• Do the voiced phonemes sound like recognisable Arabic or Syriac words?
• Are the proposed readings phonetically plausible given the encoding rules?
• Can you identify words I have marked as unknown (marked ___)?
• Does the colophon structure match known East Syriac manuscript conventions?
ENCODING RULES & PHONEME MAPPINGS
The script encodes phonemes (sounds), not a one-to-one letter substitution.
Glyph Phoneme Notes
───── ─────── ─────
d /d/ dāl — also covers dhāl (d/dh merged)
r /r/ rāʾ
l /l/ lām
k /k/ kāf — also covers qāf (q/k merged)
m /m/ mīm
s /s/ sīn — also covers zāy (z/s merged). Standalone s = bi- particle
t /tˤ/ ṭāʾ — also covers tāʾ (ṭ/t merged)
p /b/ bāʾ
f /f/ fāʾ
y /j/ yāʾ
n /n/ nūn (97.7% follows i — degree counter terminator)
a /a, aː/ fatḥa / alif — moisture degree counter
e /a/ (short) thermal degree counter: e=°1, ee=°2, eee=°3
o /ʕ/ or /uː/ ʿayn (initial) or wāw (medial)
i /iː/ kasra / yāʾ vocalic — degree counter stroke
g — word-final marker only (possibly tanwīn)
ch /ħ, x/ ḥāʾ/khāʾ merged (bracket glyph)
sh /ʃ/ shīn (bracket glyph with curl)
cth /θ/ thāʾ (t inside bracket)
ckh /dʒ/ jīm (k inside bracket)
cph /sˤ/ ṣād (p inside bracket)
cfh /dˤ/ ḍād (f inside bracket)
Informal Arabic shorthand rules:
– Gemination (shaddah) is never marked: khall → khal, ʿaṭṭār → ʿaṭār
– Hamza is elided: dāʾira → dāira, maʾ → ma
– Hāʾ is dropped in unstressed positions
– Tāʾ marbūṭa dots absent
– Accusative case endings absent (colloquial register)
– Writing direction reversed: left-to-right (not right-to-left)
THE TEXT
Each line shows:
(1) EVA glyphs
(2) Voiced phonemes
(3) Arabic / Syriac identifications
(4) Proposed reading
Unknowns are marked ___.
LINE 1
EVA: fachys ykal ar ataiin shol shory ses y kor sholdy
Voiced: faḥyṣ ykāl ʿar ʿaṭāʾīn šūl šūry ses y kūr šūldy
Arabic: فحص يقال عر عطائين — — — — كورة —
Syriac: — — — — ܫܐܠܐ ܫܘܪܝܐ — — — ܫܐܠܬܐ
Read: faḥṣ yukāl ʿar ʿaṭāʾīn sheʾlā shūrāyā asās — kūra sheʾltā
LINE 2
EVA: sory ckhar or kair chtaiin shar ois cthar cthar dan
Voiced: ṣūry ǧār ʿr kāyr ḥṭāyīn šār āys θār θār dān
Arabic: صورة جار عرض خير خطّاين شرح ايش ثروة ثروة —
Syriac: — — — — — — — — — ܕܝܢ
Read: ṣūra jār ʿarḍ khayr khaṭṭāyin sharḥ aysh tharwa tharwa dēn
LINE 3
EVA: syaiir sheky or ykaiin shod cthoary cthes daraiin sa
Voiced: syāʾīr šeky ʿr ykāʾīn šūd θūāry θes dārāyīn sā
Arabic: سيّارة? — عرض — — ثوري ثمن? داراين —
Syriac: — ܫܟܝܚ? — ܟܝܢܐ? ܫܘܕ — — — —
Read: sayyāra? shkīḥ? ʿarḍ kyānā? shōd thawrī thaman? dārayn sā
LINE 4
EVA: ooiin oteey oteos roloty cthar daiin otaiin or okan
Voiced: ʿūʿīn ʿṭeey ʿṭeūs rūlūṭy θār dāʾīmān ʿṭāʾīn ʿr ʿkān
Arabic: عيون عطيّة ___ ___ ثروة دائماً عطائين عرض ___
Read: ʿuyūn ʿaṭīyya ___ ___ tharwa dāʾimān ʿaṭāʾīn ʿarḍ ___
LINE 5
EVA: dair chear cthaiin cphar cfhaiin ydaraishy
Voiced: dāyr ḥeʿar θāʾīn ṣār ḍāʾīn ydārāyšy
Arabic: دير خير ثاني صبّار — —
Syriac: ܕܝܪܐ — — — — ܝܕܪܫ?
Read: dayr khayr thānī ṣabbār [ḍād demo] yadrusī?
LINE 6
EVA: odar shol cphoy oydar s cfhoaiin shodary
Voiced: ʿudār šūl ṣūy ʿydār s ḍūʿāyīn šūdāry
Syriac: ܥܘܕܪܢܐ ܫܐܠܐ ܨܒܝܢܐ ܥܕܪܐ ܘ — ܫܕܪ
Read: ʿudrānā sheʾlā ṣebyānā ʿeḏrā w- ___ shdārā
LINE 7
EVA: yshey shody okchoy otchol chocthy oschy dain chor kos
Voiced: yšey šūdy ʿkḥūy ʿṭḥūl ḥūθy ʿsḥy dāʾīm ḥūr kūs
Arabic: يشير — — عطول? — — دائم حور —
Syriac: — ܫܘܕܝܐ ܥܘܩܒܐ? — ܚܘܬܐ? ܥܫܝܢ? — — ܟܣܐ
Read: yushīr shōdāyā ʿuqbā? ʿuṭūl? ḥōthā? ʿeshyānā? dāʾim ḥawr kāsā
LINE 8
EVA: daiin shos cfhol shody dain os teody
Voiced: dāʾīmān šūs ḍūl šūdy dāʾīm ʿs ṭeūdy
Arabic: دائماً شروط? ظلّ? — دائم — —
Syriac: — — — ܫܘܕܝܐ — ܐܬ? ܬܘܕܝܬܐ
Read: dāʾimān shurūṭ? ḍill? shōdāyā dāʾim ōthā? tōdīthā
LINE 9
EVA: ydain cphesaiin ol cphey ytain shoshy cphodales es
Voiced: ydāʾīm ṣesāʾīn ʿl ṣey yṭāʾīn šūšy ṣūdāles es
Arabic: يدوم صفات? على صفاء? — — صيدلة —
Syriac: — — — — — ܫܘܫܦܐ? — ܐܝܬ?
Read: yadūm ṣifāt? ʿalā ṣafā? ___ shōshēp̄ā? ṣaydala ʾīth?
LINE 10
EVA: oksho kshoy otairin oteol okan shodain sckhey daiin
Voiced: ʿkšū kšūy ʿṭāʾīrīn ʿṭeūl ʿkān šūdāʾīn sǧey dāʾīmān
Read: ___ ___ ___ ___ ___ ___ ___ dāʾimān
LINE 11
EVA: shoy ckhey kodaiin cphy cphodaiils cthey she oldain d
Voiced: šūy ǧey kūdāʾīmān ṣy ṣūdāʾīīls θey šē ʿldāʾīn d
Read: ___ ___ ___ ___ ___ ___ shē ___ d-
LINE 12
EVA: dain oiin chol odaiin chodain chdy okain dan cthy kod
Voiced: dāʾīm ʿūyīn ḥūl ʿūdāʾīmān ḥūdāʾīn ḥdy ʿkāʾīn dān θy kūd
Read: dāʾim ʿuyūn ḥawl ___ ___ ___ ___ dēn ___ ___
LINE 13
EVA: daiin shckhey ckeor chor shey chol chol kor chal
Voiced: dāʾīmān šǧey keūr ḥūr šey ḥūl ḥūl kūr ḥāl
Read: dāʾimān ___ ___ ḥawr ___ ḥawl ḥawl kūra khall
LINE 14
EVA: sho chol shodan kshy kchy dor chodaiin sho keeap
Voiced: šū ḥūl šūdān kšy kḥy dūr ḥūdāʾīmān šū keeāb
Read: ___ ḥawl ___ ___ ___ dawr ___ ___ ___
LINE 15
EVA: ycho tchey chokain pshol dydyd cthy daicthy
Voiced: yḥū ṭḥey ḥūkāʾīn bšūl dydyd θy dāyθy
Arabic: — تحيّة — — — — —
Syriac: ܘܗܘ — ܚܘܟܡܐ ܒܫܐܠܐ ܕܝܕܝ ܕ ܬܝ? ܕܐܝܬܝ
Read: wa-hū taḥīyya ḥūkkāmā b-sheʾlā dīdī-d thī? d-āythī
LINE 16 ★ KEY LINE
EVA: yto shol she kodshey cphealy dar dasain dain ckhyds
Voiced: yṭū šūl šē kūdšey ṣeāly dār dāsāʾīn dāʾīm ǧyds
Syriac: ܝܬ ܫܐܠܐ ܫܐ ܩܘܕܫܐ ܨܠܘܬܐ — ܕܐܣ̈ܝܐ — —
Arabic: — — — — — دار — دائم جيّدين?
Read: yāt sheʾlā shē qudshā ṣlōtā dār d-āsāyē dāʾim jayyidīn?
LINE 17 ★ KEY LINE
EVA: dchar shcthaiin okaiir chey rchy potol cthols dlocta
Voiced: dḥār šθāʾīn ʿkāʾīr ḥey rḥy bṭūl θūls dlūktā
Arabic: ذخيرة ___ — — — — — —
Syriac: — — ܥܩܪܐ ܚܝܠܐ ܪ̈ܚܡܐ ܒܬܘܠܬܐ ܬܠܝܬܝܘܬܐ ܕܘܟܬܐ
Read: dhakhīra ___ ʿaqqārā ḥaylā raḥmē btūltā tlītāyūtā duktā
LINE 18
EVA: shok chor chey dain ckhey otol daiiin
Voiced: šūk ḥūr ḥey dāʾīm ǧey ʿṭūl dāʾīmīn
Arabic: شكر حور — دائم — عطا? دائمين
Syriac: — — ܚܝܠܐ — ܓܠܐ? — —
Read: shukr ḥawr ḥaylā dāʾim gālē? ʿaṭā? dāʾimīn
LINE 19
EVA: cpho shaiin shokcheey chol tshodeesy shey pydeey chy ro dar
Voiced: ṣū šāʾīn šūkḥeey ḥūl ṭšūdeesy šey bydeey ḥy rū dār
Read: ___ ___ ___ ḥawl tashkhīṣī? ___ ___ ___ ___ dār
LINE 20
EVA: doin chol dain cthal dar shear kaiin dar shey cthar
Voiced: dūn ḥūl dāʾīm θāl dār šeʿar kāʾīn dār šey θār
Read: dēn ḥawl dāʾim thālith? dār shaʿāʾir? kāʾīn dār ___ tharwa
LINE 21
EVA: choo kaiin shoaiin okol daiin far cthol daiin ctholdar
Voiced: ḥūū kāʾīn šūʿāʾīn ʿkūl dāʾīmān fār θūl dāʾīmān θūldār
Read: ___ kāʾīn shuʿāʿīn? ʿuqūl dāʾimān farr/farḍ? thaqīl dāʾimān thaqīl-dār?
LINE 22
EVA: ycheey okay oky daiin okchey kokaiin chol kchy dal
Voiced: yḥeey ʿkāy ʿky dāʾīmān ʿkḥey kūkāʾīn ḥūl kḥy dāl
Read: yuḥyī? ʿaqqār? ___ dāʾimān ___ ___ ḥawl ___ dāll
LINE 23
EVA: deeo shody koshey cthy okchey keey keey dal chtor
Voiced: deeū šūdy kūšey θy ʿkḥey keey keey dāl ḥṭūr
Syriac: — ܫܘܕܝܐ ܟܘܫܝܐ? — — — — — —
Arabic: — — — — — — — دالّ خطور?
Read: ___ shōdāyā kūshāyā? ___ ___ hot°2 hot°2 dāll khuṭūr?
LINE 24
EVA: chol chok choty chotey dchaiin
Voiced: ḥūl ḥūk ḥūṭy ḥūṭey dḥāʾīn
Arabic: حول حكّ خطا خطط —
Syriac: — — — — ܕܟܐܐܝܢ
Read: ḥawl ḥakk khuṭā khuṭāṭ d-kāʾīn
Unknowns (___) are the primary targets for review. All feedback welcome.
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| Possibility that the VM author was colorblind? |
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Posted by: hatoncat - 02-04-2026, 08:59 AM - Forum: Theories & Solutions
- Replies (1)
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You are not allowed to view links. Register or Login to view.
"The situation is much more complex than just an overenthusiastic blue painter: - there is also too much white (blank)
- frequent, seemingly intentional combination of blue and blank elements
- there is too much yellow in the stems, a color notably lacking from the flowers
- calyxes in other manuscripts are green as a rule. While almost every large-plant page has green available to it (for the leaves), a disproportionate amount of other colors is preferred for calyxes."
After reading this post, I wondered if perhaps the blue flowers in the manuscript might have actually appeared green to the drawer. This might not have made a lot of sense, but according to a google search "what do color blind people see blue as":
"People with blue-yellow color blindness (You are not allowed to view links. Register or Login to view./You are not allowed to view links. Register or Login to view.) generally see blue as green, light blue as grey, or find that blues appear much darker and less vibrant. While rare, this type of deficiency specifically makes it difficult to differentiate blue from green and yellow from violet or pink."
Could this explain why they thought they were including pink when they were using a yellow pigment, or a green ink when it was actually blue?
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| Voynich text features summary file |
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Posted by: quimqu - 02-04-2026, 08:13 AM - Forum: Analysis of the text
- Replies (5)
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Hello all,
to all of us who are digging in the analysis of the MS text, I think it might be interesting to have a brief summary of the confirmed text features. That's why I have created this file: You are not allowed to view links. Register or Login to view.
It is open to be improved with your thoughts (proven facts) (in order to keep a proper view, it is not open to edit). I thik it may be a useful tool for keep on analysing the text and not forget already proven features (and not repeat too much testing).
Feel free to comment, add fetures (via reply in the thread) and join the discussion.
(By the way, my two lines in the "generative facts" page, if you think they are not confirmed or are uncertain, please tell me)
Thank you
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| Cross-model notation structure in the herbal section - a statistical approach (prepr) |
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Posted by: FamagustaTed - 01-04-2026, 07:00 PM - Forum: The Slop Bucket
- Replies (2)
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You are not allowed to view links. Register or Login to view.
Hi Everyone, I'm new here. I've been working on a structural analysis of the Voynich manuscript and wanted to share the results.
The paper takes a statistical approach to the herbal section, treating the script as a system to be characterised rather than a language to be identified. The core finding is that label morphemes encode plant-architecture features (stem type, root form, leaf shape, complexity) that can be tested against the illustrations - and that prose behaves differently depending on how much descriptive work the labels already do.
It's built on falsification throughout - every claim has a permutation test, several hypotheses were killed along the way (including my original language candidate), and there's an explicit claims ledger in the appendix showing what survived and what didn't.
What the paper does NOT claim: decipherment, a source language, or readings. The last line is literally "What it does not yield - and may never yield through structural analysis alone - is a reading."
Happy to take questions or pushback - that's what it's for.
Paper description
This paper presents a falsifiable structural model of the Voynich manuscript (Beinecke MS 408), based on computational analysis of the complete ZL IVTFF 2b transcription (36,234 tokens, 226 folios, 8 sections). Rather than attempting to identify an underlying natural language, the study asks what kind of system the manuscript implements, and answers through holdout-validated formal analysis, independent unsupervised confirmation, and cross-modal testing against the manuscript's illustrations.
The model establishes five principal findings. First, a four-layer morphological grammar classifies 91-97% of tokens across six stratified holdout blocks spanning five manuscript sections, three or more scribal hands, and both Currier languages, with zero stacking-order violations in any block and no parameter adjustment after model freeze. Second, the invariant formal system is deployed in at least six distinct compositional regimes -- loop-based prose, topic-dominant chaining, nominal labelling, weakened-loop variant, closure-weighted operational mode, and balanced connective mode -- varying systematically by section and hand. Two regimes were discovered only upon unsealing the sealed reserve holdout, demonstrating that the taxonomy expands under evaluation. Third, discourse-framing density in text predicts visual complexity of herbal illustrations (Spearman rho = 0.600, p < 0.0001, n = 43), confirmed by pre-registered holdout with minimal attenuation. At the label level, specific morphemes predict specific plant features across five independent visual channels, and morpheme bundles predict multi-feature plant profiles compositionally (LOO AUC p = 0.0006). Fourth, a 17-mapping codebook decodes plant architecture from herbal labels at 58.5% accuracy across 72 folios and is bidirectional: image features recover label morpheme sets above chance (p < 0.0001), with forward-greater-than-inverse asymmetry diagnostic of selective encoding rather than cipher. Labels and prose perform complementary, load-balanced functions confirmed by an adaptive compensation mechanism (rho = -0.337, p = 0.011). Fifth, the system meets 8 of 10 criteria for restricted technical notation while failing the criterion most diagnostic of natural language: lexical recoverability.
These findings are independently triangulated: a rule-based grammar, holdout replication across two evaluation stages, and unsupervised HMM recovery of grammar classes from suffix sequences alone (NMI = 0.181, entity purity 0.53) converge on the same structural conclusions. The architecture is inconsistent with simple cipher, random generation, hoax, or classical mnemonic systems.
The study also situates the manuscript within the documented manuscript ecology of the eastern Mediterranean, presenting quantitative visual comparisons against six comparator manuscript traditions. The herbal section aligns closely with early encyclopedic Qazwini copies (Euclidean distance 2.37), while the zodiac section occupies a distinct visual regime matching no tested tradition, combining Latin computational diagram architecture with Byzantine Greek medico-astrological content and a unique figurative encoding system.
The manuscript is best understood as a structured, sectionally differentiated technical system with partially recoverable semantics -- structurally technical but lexically local. Its grammar is real and invariant. Its regimes are real and section-specific. Its text and images interact. Its labels carry structured semantic content. What it does not yield through structural analysis alone is a reading.
This deposit includes the pre-submission draft (v5.0), analysis scripts, data files, and figures.
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| The structure of the Voynich text and how it may be generated |
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Posted by: quimqu - 01-04-2026, 12:16 PM - Forum: Analysis of the text
- Replies (10)
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Most discussions about the Voynich already agree on a few basic points: the text is not random, similar words tend to appear near each other, and position within the line matters. What is less clear is how this structure is actually generated.
I have been working in a pipeline that lets me analyze the structure of the MS. The goal of this analysis was not to describe patterns, but to test concrete generative hypotheses against the data. The question was always the same: if this were the real mechanism, would it reproduce what we observe? Running different models through the same pipeline makes it possible to discard entire classes of explanations, not just speculate about them.
| Hypothesis | Expected behavior | Observed failure |
| Random / weak structure | No stable local similarity or positional effects | Strong clustering and positional patterns persist |
| Sequential (Markov-like) | Next token predictable from previous ones | Bigram/HMM models add little or collapse |
| Copy–modify (parent-based) | Clear local derivations, strong nearest neighbor | Generative models produce too much similarity |
| Single dominant parent | One best local candidate per token | Multiple candidates with similar scores, no clear winner |
The important point is not just that these models fail, but how they fail. Copy-and-modify mechanisms generate too much similarity, producing tight chains of derived forms that are not observed in the real text. Sequential models fail in the opposite direction, missing most of the structure entirely. The idea of a single dominant parent breaks down because the local neighborhood is too ambiguous: for most tokens, several nearby forms are equally plausible, with no clear winner. These are structural mismatches, not minor errors, and they rule out a large class of simple generative explanations.
At the same time, some effects are very robust. Local similarity is real and strong: words share substrings and cluster in form space. Position within the line has a clear impact on length, prefixes, and suffixes. But these signals do not translate into a simple mechanism where one word determines the next. Token-level models struggle precisely because the system is not organized primarily as a chain of local decisions.
The structure becomes clearer when moving to the level of the full line. If lines are represented as whole objects, using their internal properties (number of tokens, length distributions, entropy, positional patterns), they fall into a small number of latent types. These types are not imposed manually, but learned directly from the text. They correspond broadly to different functional roles, but also reveal variation within them. These data-driven line types also show persistence across consecutive lines, suggesting that the manuscript is organized as sequences of line-level states, not just as a stream of loosely connected tokens.
- The text is not governed by simple sequential rules. Token-to-token models fail to capture the structure, even when extended beyond basic Markov assumptions.
- It is not generated by copy-and–modify or parent-based derivation. These mechanisms overproduce similarity and impose chains that are not present in the data.
- There is no single dominant local source for most tokens. The local neighborhood is too ambiguous, with multiple equally plausible candidates.
- The strongest and most stable structure appears at the level of the full line. Lines form a small number of latent types with distinct formal profiles and non-random sequencing.
A useful way to think about it is the following:
- The line type defines a space of possible forms.
- The local context restricts this space further by favoring forms that are compatible with nearby words.
- But within that constrained space, the final choice is weakly determined. Many candidates are acceptable, and no single one is strongly preferred.
This explains why local similarity is strong but does not translate into clear parent-child relationships, and why token-level models struggle while line-level structure is much more stable.
With this analysis I try not to show that the Voynich is structured, which was already suspected, but to narrow down the class of mechanisms that can plausibly generate it. Simple sequential models and naive copy-and-modify processes do not fit. Models that operate at the level of line-level states, combined with a local compatibility field and weak selection within that field, are much more consistent with the data.
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