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Johannes Neumeier


Johannes Neumeier
Last Active
  • Re: Are foundry initials an inherent part of a type family name?

    In cases of revivals I understand the need to differentiate between different releases of similar design or namesake. When some foundries do this as a matter of principle for all their releases then this kind of blunt force marketing seems so off-putting to me, personally. Some grocery chains have their own line of low budget bulk products under their own in-house brand... it feels like that.
  • Re: Musk, High Contrast Sans Serif

    The relative weight of bolds and lights is also influenced by the use case. For example a bold geared towards screens use might be more explicitly dark to convey a proper weight increase on any number of resolutions and rendering algorithms, whereas a high quality editorial print might be a little more subtle and its weight increase can still be obvious enough.
    The other thing that also makes weight relative is the intended use size, and how cluttering up adds extra weight in small sizes, which you might compensate for to keep the weight distribution as intended.

    But with both those issues it is a chicken and egg problem when it comes to designing the type.
  • Re: Musk, High Contrast Sans Serif

    The stroke weight seems to vary by three groups, from too heavy to too light: Diagonals, verticals, round glyphs. So V looks too dark compared to H, but O looks to light compared to H. This is equally visible in the lowercase, and in the text at small size you can notice it as the stems being clearly darker.

    The diagonals, especially at the end of the alphabet, feel stylistically out of place and generally too straight. If you compare K to V, for example, the V stands out as too rigid. Maybe you can try some stroke variation in the verticals and diagonals, so they mingle better with those luscious terminals you got going. At the same time, try to not overdo it with that vibe, if you mean to keep it readable for text, which right now still nicely works for small passages.

    Also, the M, since it is the namesake letter of the face; it has a very open crotch.

    More consistency is needed, but the direction is nice; a typeface with very pleasant tones and its own character.
  • Re: I trained a neural network to kern a font (mostly)

    Without retraining the network some quick tests seem to mostly point you towards "potential" kerning pairs. Aside from the p% value I'd much more like to know how confident the script is about the amount it suggest a pair should be kerned - is it, based on the training data, a heavily scattered or homogenous pair. I see the end goal of the p% for automating kerning classes, though. Based on how often a character shares kerns with high confidence with other characters those could be suggested as groups.

    I did only run the script on two fonts and manually went in to compare only a few of the kerning finds. Some observations:
    • Characters that I'd often myself consider to have mirrored kerning, so like A V T O o, seem not to be recognised as such (mirrored kern values for also the same symmetrical glyph on either side). I can't surmise how the learning material would not reflect that also, but maybe other factors have more weight in the output, e.g. kerns that are mirrored or near equal should probably emphasise that in evaluation to skew the result to symmetry, where such is identifiable from the training material
    • Some kerns that I would think are common and are also non-negligent in value from my own manual kerning seem to have been completely disregarded, e.g. F-o A-T (first image)
    • Identifying classes seems to work reasonably well, meaning it finds similar shapes to receive the same kerning

    A-T kern missing completely (the serif?), while T-A does have an okayish value, A-V-A a tad more lose than my manual:

    A-V-A-T-A all with kerns, but A-V-A and A-T-A disturbingly unsymmetrical:

    Example of visually quite obvious and also not uncommon missing F-o kern, that wasn't in the table at all:

    Good example of appropriate identification of kern classes, even though the actual kern value is small:

    A c -10 p= 83 %A d -10 p= 95 %A e -10 p= 87 %...A g -10 p= 92 %A o -10 p= 95 %A q -10 p= 95 %

    Mixed results of kern class identification:

    F n -25 p= 99 %F p -25 p= 99 %...F r -25 p= 81 % < Good: Less certain, but left stem identified correctly as same group with n and p...F v -25 p= 55 %F w -25 p= 69 %F x -65 p= 61 % < How come different class?F y -25 p= 83 % < ~25% more certainty than for F-v and F-w?F z -20 p= 40 %

    Very interesting work! I'm curious how this develops.
  • Re: EULAs: No Modifications Clauses.

    This one might be missing: To charge for executing to said modifications.