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Did not work well at all for me, and now I'm left wondering if it's the neural networks fault, or if I'm just very bad at writing kanji now.
by SuperNinKenDo 8y ago
Did not work well at all for me, and now I'm left wondering if it's the neural networks fault, or if I'm just very bad at writing kanji now.
- prewett 8y agoI don't think it's your kanji writing; I can't figure out what it's supposed to do. I'm assuming it's supposed to autocomplete my kanji, but it failed at that, for both 国, 本, and 三. I'm pretty confident I wrote them right, especially the last one :) It seems to just write random kanji based on the last stroke or something. But, writing random kanji has a certain coolness factor. Especially if I could copy-paste a kanji and get it to write it for me so that I know what the stroke order is supposed to be.
- kazinator 8y agoHit or miss for me. It doesn't want to fill in anything if I draw the enclosure of "wind": 風. Also fails to find some of the simplest kanji; completions for just a stroke or two are quite complex. E.g. a simple downstroke suggests mouth 口, with just two more strokes, or 土 and whatnot, but .. 出てきてくれない. :) It is also stumped if I draw the first two strokes of 山; it wasn't trained on this character, to complete that middle stroke, and basically doesn't want to do anything else, either. Some out-of-order inference would be cool. E.g. draw the bottom four dots (fire) of 煎, and have various top parts emerge. For that purpose, it would be good if there were a reference frame. That is to say: an underlying square box to serve as a target for the supplied input. If you draw something near the bottom of the empty box, then it's understood by the neural network to be a bottom part of the kanji requiring a top. I think the whole concept could really benefit from a precise agreement between the user and the neural net about the bounding box.