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B#: Chord-based Correction for Multitouch Braille Input

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B#: Chord-based Correction for Multitouch Braille Input

  1. 1. B# :: Chord-based Correction for Multitouch Braille Input HUGO NICOLAU KYLE MONTAGUE TIAGO GUERREIRO JOÃO GUERREIRO VICKI L. HANSON
  2. 2. motivation :: non-visual input
  3. 3. motivation :: braille input [Southern et al. 2012] [Oliveira et al. 2011] [Azenkot et al. 2012]
  4. 4. motivation :: fast typing (4x)
  5. 5. problem :: error prone %
  6. 6. challenge :: accurate input
  7. 7. approach :: spellchecker ? ? ? ? ? ? ? ? ? ? > e d k l o w u h k d _
  8. 8. approach :: character-level e x i l e d u n k s > e d k l o w u h k d _
  9. 9. B# :: chord-level spellchecker h e l l o w o r l d > e d k l o w u h k d _
  10. 10. B# :: data collection
  11. 11. B# :: data collection 11 blind participants :: ages 22 – 62 :: 3 females :: grade 1 braille :: no touchscreen experience
  12. 12. major results :: substitutions 23% 72% 5% Insertions Substitutions Omissions Overall Error Rate: 18%
  13. 13. major results :: 43% single-dot error omission insertion transposition q g i f b k
  14. 14. major results :: invalid characters ! t
  15. 15. B# :: braille distance [100010 ] [100010 ] = ? -
  16. 16. distance :: damerau-levenshtein [100] – [110] + [010] – [010] = 1 + 0 = 1
  17. 17. B# :: scoring function scoreword = wMSD(Chtyped, Chword) + ƒword
  18. 18. evaluation :: B#, AOSP
  19. 19. evaluation :: dataset 751 words :: 49% incorrect entries :: word list with 213,133 entries
  20. 20. results :: B# is more accurate 56% 75% 28% 39% 25% 35% 45% 55% 65% 75% 1 2 3 4 5 6 7 8 9 10 Accuracy B# Android
  21. 21. results :: word correction 204 of 364 100 of 364
  22. 22. results :: no gain beyond 5 25% 35% 45% 55% 65% 75% 1 2 3 4 5 6 7 8 9 10 Accuracy B# Android 6% 9 %
  23. 23. results :: incorrectly corrected 1.4% 1.7% Android B#
  24. 24. conclusion :: B#
  25. 25. conclusion :: higher accuracy X
  26. 26. future :: non-visual interface
  27. 27. The End. HUGO NICOLAU hugonicolau@computing.dundee.ac.uk paper, slides, and video @ http://web.ist.utl.pt/hugo.nicolau
  28. 28. non-visual correction :: challenges Automatic correction? Word-completion? How to provide corrections? When to provide them? How to users explore suggestions?
  29. 29. B#: future improvements Bi-grams, Tri-grams, N-grams “Machine Learned” Distance Function Personalized spellchecker Leverage touch information
  30. 30. B#: android’s proximity matrix Fairly complex spellchecker Dozen of features Worthwhile doing

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