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Belleve Invis

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Belleve Invis
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  • Re: [OTVar] OpenType 1.8.1 released

    Unfortunately, blending a subroutine index is not forbidden in the 1.8.1 CFF2 CharStrings chapter. The intent is in fact to allow blending of only coordinate values. This follows from a higher level principle that we want to always be able to convert a set of outlines between TrueType and CFF2 CharStrings, and hence do not want to allow operations in CFF2 Charstrings that cannot be supported in TrueType. There was a sentence which stated this in most versions of the 1.8.1 working draft, but it got lost by accident before the final version. At this point, it will have to wait for the 1.8.2 update, but this restriction will go back into the spec.
    And CFF2 allows that you can re-use a blended result as a parameter of another blend operator, which introduces non-linear interpolation that TT’s GVAR cannot support.
    And maybe you guys should provide some library to perform topology-preserving outline format conversion, esp. cubic → quadratic.
  • Re: Dedicate library for identifying strokes/stems?

    Identify just in terms of glyph topography, or identify in terms of being able to label the stems?
    Identify stems from a specific input glyph (outlines, in particular), return a list containing information including:
    1. Upper/Left edge points
    2. Lower/Right edge points
    3. Upper/Left edge position and slantness
    4. Lower/Right edge position and slantness
  • Dedicate library for identifying strokes/stems?

    Sometimes I am wondering that there would be a library for stroke/stem identification. Input an outline and output the identified strokes/stems with point index correspondence.
    Should I extract them from autohinters?
  • How can I publish my font to Google Fonts?

    You know, I made this, but there I cannot find the way to publish it.
  • Automatic generation of large-scale handwriting fonts via style learning (SIGGRAPH Asia 2016)

    Generating personal handwriting fonts with large amounts of characters is a boring and time-consuming task. Take Chinese fonts as an example, the official standard GB18030-2000 for commercial font products contains 27533 simplified Chinese characters. Consistently and correctly writing out such huge amounts of characters is usually an impossible mission for ordinary people. To solve this problem, we propose a handy system to automatically synthesize personal handwritings for all characters (e.g., Chinese) in the font library by learning style from a small number (as few as 1%) of carefully-selected samples written by an ordinary person. Experiments including Turing tests with 69 participants demonstrate that the proposed system generates high-quality synthesis results which are indistinguishable from original handwritings. Using our system, for the first time the practical handwriting font library in a user’s personal style with arbitrarily large numbers of Chinese characters can be generated automatically. 
    Link: http://dl.acm.org/citation.cfm?id=3005371