1. 1 Optimal Character Arrangement for Ambiguous Keyboards Reporter: En-ping Su 2005 10.18

2. 2 Outline Introduction Background Methods Results and discussion Conclusions

3. 3 I. Introduction The operation of QWERTY keyboard is a frustrating and time-consuming for many persons with disabilities An ambiguous keyboard with a disambiguation algorithm may help them to increase keystroke efficiency

4. 4 I. Introduction Three purpose of this paper  describe and demonstrate the generic optimization  present an optimal arrangement for the standard nine key ambiguous keyboard  establish an accurate set of keystroke efficiency bounds for ambiguous keyboards having a given number of keys

5. 5 II. Background Word-level disambiguation  Witten[1982] introduced the scheme and was modified slightly by Minneman[1985] Character-level disambiguation  Levine et al.[1987] proposed  ngrams

6. 6 II. Background Character-level disambiguation(cont ’ d)  ngrams: the n-1 preceding characters are used to predict the current( ) character  n=2, was named digram  n=3, was named trigram  n=4, was named quadgram

7. 7 II. Background The best performanceThe worst performance

8. 8 II. Background The performance of a keyboard can be quantified by measuring the Keystroke efficiency Keystroke efficiency  the number of characters produced divided by the total number of keystrokes Keystroke efficiency=N/(N+R) N: the total number of characters in a sample text R: the total number of retries

9. 9 III. Methods The Optimization Problem  26 letters divided over nine keys: 3 3 3 3 3 3 3 3 2 → there are approximately possible arrangements  it would be impossible to try each of these arrangements

10. 10 III. Methods Confusability Matrices  is a single M x M matrix and express the absolute costs (in terms of extra keystrokes) of having any two characters together on the same key

11. 11 III. Methods Confusability Matrices(cont ’ d)  ex: “ t ” : was the intended character (a, s, t, v, … ): was the probability-ordered list of predicted characters → (t, a), (t, s) in the matrix would be incremented  Finally, each entry C(α, β) will represent the total number of times character β was predicted before character α when α was the intended character

12. 12 III. Methods Optimization Methods  n-optimization(n-opt): just select any n characters and calculate if shuffle those characters such that the keyboard efficiency is better than the current arrangement  n-opt ex: n=2 → (ab, ac, ad, … az, bc, bd, be, … xy, xz, yz)

13. 13 III. Methods Optimization Methods(cont ’ d)  n-opt approaches suffer from a significant drawback → the algorithm gets stuck in a local maxima instead of searching for the global maximum  a simple method to solve it is to run an n- opt algorithm with many different random initial arrangements

14. 14 III. Methods Testing Procedure  the ngram and kgram statistics were generated through an analysis of a 1.5 million word of Time magazine from 1991 to 1993  the sample text( ) used to generate the confusability matrices consisted of 50,000 words from the same database

15. 15 III. Methods Testing Procedure(cont ’ d)  to ensure unbiased results, we employed a series of seven testing documents 1 : compilation of stories, essays, and letters written by students of various ages : a narrative story from Reader ’ s Digest : a scholarly work from Goffman : another non-overlapping collection of articles from Time

16. 16 IV. Results and discussion

17. 17 IV. Results and discussion Table 1: Average keystroke efficiency for the four keyboard arrangements using four different character prediction algorithm

18. 18 IV. Results and discussion Fig. 3. The optimal keyboard arrangement for kgram, quadgram, trigram, and digram character prediction algorithms

19. 19 IV. Results and discussion

20. 20 V. Conclusions These techniques can be applied to quickly generate optimal arrangements with high keystroke efficiency Ambiguous keyboards may play a broader role as a generic interface methodology in portable electronic devices