for different letter replacements. For example, you can say that the penalty for substituting an 'a' Air Max Tavas Custom
jere 734 days ago link
I think he's suggesting that for "custom words" flesky autocorrects to the wrong words, whereas the standard keyboard doesn't thus leading to longer input times under flesky . at least until it learns those words.
rm999 734 days ago link
The Levenshtein distance, the standard metric for determining how different words are from each other, can easily be modified to change the penalty Nike Air Max 2015 White Black Volt
Awesome new predictive text input method
seconds to .35. Plus, you only need to input those terms once.
This is pretty much how the stock Android keyboard works. I just typed "kettrt" and it's suggesting "letter" as a correction. I wonder if it's packing a huge machine learning library though. wouldn't that take a ton of space? I saw in another comment that it takes a while to start up maybe that's why
Actually, touch typing on Fleksy is faster than on the standard keyboard. We reduced the lag time required to input letters from .5 Air Max Thea White Grey
I had this on my PalmPilot in 1998 but from a different company. It was written by a friend who worked at a company with all kinds of patents for it, but they never released the Palm version publicly. Only a few dozen people had it in the world. Most of the mistakes it makes are due to the fact that it seemingly doesn't look at your address book for completion options, so when you try to type in people's names you know it picks a different word and doesn't even have proper names as an option. As a totally blind iPhone user this app has some promise and some major flaws. The first issue is the fact that the app takes at least 30 seconds to load on my iPhone 4 if you haven't used it in a while. I'll deal with the crappy typing experience using the iPhone onscreen keyboard instead of waiting 30 seconds if I'm just sending a quick text. The second issue is the inefficiency of entering custom words. If a word isn't in Fleksy's dictionary it's much faster to type the word using the normal keyboard instead of with Fleksy. This makes writing technical emails or messages with lots of nonstandard words quite difficult. Since you can add words Nike Air Max Tavas Camo to a custom dictionary in Fleksy though I would expect this to get better with time.
jeremyarussell 734 days ago link
samstokes 734 days ago link
chasingtheflow 734 days ago link
fleksy 734 days ago link
Can anyone explain how this works? Seems like total black magic to me. the dude is typing random gibberish and it still figures out the word? Then he's just tapping randomly on the screen, gibberish comes out, then turns into the word he wanted?Don't mean this to sound unintelligent, the video was just crazy. What kind of key proximity algorithms are behind this?
for 's' is 1, and 'a' for 'p' is 10 (or whatever).
fleksy 734 days ago link
ricardobeat 734 days ago linkjenius 734 days ago link
I used incorrect terminology. With the on screen keyboard I am able to drag a finger around until I hear the letter I want announced, then with another finger tap the top half of the screen to enter that letter. It is much more efficient then touch typing with either Fleksy or touch typing with the standard keyboard.
andrewmunsell 734 days ago link
jareds 734 days ago link
The thing he mentioned was startup time, though, not the actual input lag.
Was responding to the second issue: "The second issue is the inefficiency of entering custom words". On that front Fleksy is the same or faster than the standard iOS keyboard.
Patent pending kind? I doubt they are sharing it quite yet but the one beauty of patents is we'll get the entire answer once the patent isn't pending anymore. If I had to imagine though, the algorithm is just accounting for the most likely word to follow the previous wording, that could have been formed by hitting keys that were next to the ones you really hit wrong. I'd like to see how it handles my wanton throwing of swear words in messages to friends. Letter ends up being 35% as opposed to the 7 percent chance you see any of those other words. ( that last bit of percentage math is an estimation given for the sake of example, in a true text prediction dictionary the percentages are usually small, given the incredibly large amount of words that can and do follow "you miss every" (chance, shot, etc are probably more likely, but not near those letters we kept missing earlier.)It would seem they've made improvements on that algorithms. I wonder how novel it really is though.
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