Lingua Volgare Shorthand

[ginocaspari]

Our solution at least partially addresses the low entropy as a function of oversplitting glyphs. We believe that this in itself is an important finding that should be taken into consideration and factor into future statistical studies.

I am sorry but that is not correct, and it is consequently not an important finding. This has been studied numerically and the results are clear. Any proposed alternative opinion should also be backed up by quantitative information.

The truth is that an alphabet like Eva, which indeed is well known to split up things that are probably units, does indeed further lower conditional entropy, but in all known alphabets this entropy is anomalously low.

Sorry to be brief but I cannot do better on my mobile phone.

Dear René,
thank you for your - albeit brief - input. We have dedicated an entire chapter (6) here You are not allowed to view links. Register or Login to view. to the topic of entropy. We acknowledge that not the entire phenomenon of low entropy is explained by this, but it is a substantial part. A recalculation is in order. The oversplitting aspect affects a majority of the most frequent bigrams, which is fascinating because we did not design our solution to specifically look for oversplit glyphs or had the goal of increasing entropy.  The lowered entropy which has to do with the end of words is likely strongly affected by the shorthand which disproportionately affects word endings. We are very much in favor of a quantification of this across the entirety of the Voynich, this does however require a renewed transcription. While exact values need a lot of transcribed text, it is obvious which bigrams are affected by our solution and whether they lower or increase entropy. The direction of the result is thus already clear. 

TLDR: We believe the low entropy is due to two phenomena 1. oversplitting (which accounts for the majority) 2. shorthand (which mostly affects low entropy regarding word endings)