Building on a comment by Douglas, one approach to automating generalized fonts could be to create them by example via statistical machine learning.<\/p>\n
Imagine, when I was walking by those chess stores in Greenwich Village that started this line of thinking, if I had taken the pawn from every chess set, and had fed all those shapes in to a computer program, with the instruction “this is a pawn”. Now imagine I had done the same for all the bishops, etc.<\/p>\n
The software would have two kinds of labeling information to work from: (1) These are all pawns, and (2) This group of pieces (knight, bishop, etc.) are all from the same set.<\/p>\n
It would be interesting to see whether statistical machine learning could make use of that labeling. Not just to be able to assert, given a new piece, “this is a bishop”, or “these two pieces belong to the same set”, but to make the more interesting assertion: “Here is a chess set that nobody has ever seen before, which is a weighted mix of these other example sets”. <\/p>\n
And it would be interesting to compare this automated approach to the old fashioned one of manually analyzing the structural parts of a chess piece (as I have been doing), in order to build new variations.<\/p>\n
Each approach would have its advantages. The statistical machine learning approach could potentially get a lot further faster, but it would be unlikely to ever be able to tell us how and why it made its choices. For actual insight, I believe the manual labeling analysis\/synthesis approach will win hands down.<\/p>\n","protected":false},"excerpt":{"rendered":"
Building on a comment by Douglas, one approach to automating generalized fonts could be to create them by example via statistical machine learning. Imagine, when I was walking by those chess stores in Greenwich Village that started this line of thinking, if I had taken the pawn from every chess set, and had fed all … Continue reading “Generalized fonts: manual design versus machine learning”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=\/wp\/v2\/posts\/8281"}],"collection":[{"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8281"}],"version-history":[{"count":11,"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=\/wp\/v2\/posts\/8281\/revisions"}],"predecessor-version":[{"id":8292,"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=\/wp\/v2\/posts\/8281\/revisions\/8292"}],"wp:attachment":[{"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8281"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8281"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.kenperlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8281"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}