1. B# :: Chord-based Correction
for Multitouch Braille Input
HUGO NICOLAU
KYLE MONTAGUE
TIAGO GUERREIRO
JOÃO GUERREIRO
VICKI L. HANSON

2. motivation :: non-visual input

3. motivation :: braille input
[Southern et al. 2012]
[Oliveira et al. 2011] [Azenkot et al. 2012]

4. motivation :: fast typing (4x)

5. problem :: error prone
%

6. challenge :: accurate input

7. approach :: spellchecker
? ? ? ? ? ? ? ? ? ?
> e d k l o w u h k d _

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. 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. B# :: data collection

11. B# :: data collection
11 blind participants :: ages 22 – 62 ::
3 females :: grade 1 braille ::
no touchscreen experience

12. major results :: substitutions
23%
72%
5%
Insertions Substitutions Omissions
Overall Error Rate: 18%

13. major results :: 43% single-dot error
omission
insertion
transposition
q g
i f
b k

14. major results :: invalid characters
! t

15. B# :: braille distance
[100010
]
[100010
]
=
?
-

16. distance :: damerau-levenshtein
[100] – [110] + [010] –
[010]
= 1 + 0 =
1

17. B# :: scoring function
scoreword = wMSD(Chtyped, Chword) +
ƒword

18. evaluation :: B#, AOSP

19. evaluation :: dataset
751 words :: 49% incorrect entries ::
word list with 213,133 entries

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. results :: word correction
204 of 364
100 of 364

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. results :: incorrectly corrected
1.4% 1.7%
Android B#

24. conclusion :: B#

25. conclusion :: higher accuracy
X

26. future :: non-visual interface

27. The End.
HUGO NICOLAU
hugonicolau@computing.dundee.ac.uk
paper, slides, and video @ http://web.ist.utl.pt/hugo.nicolau

28. non-visual correction :: challenges
Automatic correction?
Word-completion?
How to provide corrections?
When to provide them?
How to users explore suggestions?

29. B#: future improvements
Bi-grams, Tri-grams, N-grams
“Machine Learned” Distance Function
Personalized spellchecker
Leverage touch information

30. B#: android’s proximity matrix
Fairly complex spellchecker
Dozen of features
Worthwhile doing