Abstract
Computer text entry may be full of noises – for example, computer keyboard users inevitably make typing mistakes and their typing stream implies all users’ self rectification actions. These may produce a great negative influence on the accessibility and usability of applications. This research develops an original Intelligent Keyboard hybrid framework, which can be used to analyze users’ typing stream, and accordingly correct typing mistakes and predict users typing intention. An extendable Focused Time-Delay Neural Network (FTDNN) n-gram prediction algorithm is developed to learn from the users’ typing history and produce text entry prediction and correction based on historical typing data. The results show that FTDNN is an efficient tool to model typing stream. Also, the computer simulation results demonstrate that the proposed framework performs better than using the conventional keyboard.
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Ouazzane, K., Li, J., Brouwer, M.: A hybrid framework towards the solution for people with disability effectively using computer keyboard. In: Proceedings of International Conference Intelligent Systems and Agents, Amsterdam, The Netherlands, pp. 209–212 (2008)
Trewin, S.: An invisible keyguard: Proceedings of ACM SIGACCESS ASSETS, Edinburgh, Scotland, pp. 143–149 (2002)
Bengio, Y., Ducharme, R., Vincent, P., Janvi, C.: A neural probabilistic language model. The Journal of Machine Learning Research 3, 1137–1155 (2003)
Schwenk, H., Gauvain, J.: Connectionist Language Modeling for Large Vocabulary Continuous Speech Recognition. In: Proceedings of ICASSP, Orlando, pp. 765–768 (2002)
Trewin, S., Pain, H.: A Model of Keyboard Configuration Requirements. In: Proceedings of International ACM Conference on Assistive Technologies, pp. 173–181 (1998)
The Dasher Project, Inference Group of Cambridge (November 14, 2007), http://www.inference.phy.cam.ac.uk/dasher/ (accessed March 03, 2008)
Ward, J., Blackwell, A., et al.: Dasher - a Data Entry Interface Using Continuous Gestures and Language Model, http://www.inference.phy.cam.ac.uk/djw30/papers/uist2000.html (accessed March 03, 2008)
QWERTY (November 13, 2009), http://en.wikipedia.org/wiki/QWERTY (accessed November 13, 2009)
Prototype, n.d., http://www.sensorysoftware.com/prototype.html (accessed March 03, 2008)
Metaphone, Wikipedia (October 18, 2008), http://en.wikipedia.org/wiki/Metaphone (accessed January 23, 2009)
N-gram, Wikipedia (November 26, 2008), http://en.wikipedia.org/wiki/N-gram (accessed January 23, 2009)
Li, J., Hirst, H.: Semantic Knowledge in Word Completion. In: Proceedings of the 7th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 121–128 (2005)
Li, J., Ouazzane, K., Jing, Y., Kazemian, H., Boyd, R.: Evolutionary ranking on multiple word correction algorithms using neural network approach. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds.) EANN 2009. Communications in Computer and Information Science, vol. 43, pp. 409–418. Springer, Heidelberg (2009)
Focused Time-Delay Neural Network (newfftd), The MathWorks, http://www.mathworks.com/access/helpdesk/help/toolbox/nnet/dynamic3.html (accessed January 23, 2009)
Haykin, S.: Neural Networks – A comprehensive Foundation, 2nd edn. Tom Robbins (1999)
Virtual key codes, http://api.farmanager.com/en/winapi/virtualkeycodes.html (accessed February 05, 2009)
Hardy, T.: Wikipedia (January 21, 2009), http://en.wikipedia.org/wiki/ (accessed January 22, 2009)
Cleary, J., Teahan, W.J., et al.: Unbounded length contexts for PPM. IEEE Computer Society Press, Los Alamitos (1995)
Bloom, C.: PPMZ–High Compression Markov Predictive Coder, http://www.cbloom.com/src/ppmz.html , ftp://ftp.cpsc.ucalgary.ca/pub/projects/text.compression.corpus/text.compression.corpus.tar.Z (accessed January 18, 2009)
Soukoreff, W., MacKenzie, S.: n.d. KeyCapture, http://dynamicnetservices.com/~will/academic/textinput/keycapture/ (accessed January 18, 2009)
Disability Essex, http://www.disabilityessex.org (accessed January 18, 2009)
Knowledge Transfer Partnership, http://www.ktponline.org.uk/ (accessed January 18, 2009)
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© 2011 International Federation for Information Processing
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Ouazzane, K., Li, J., Kazemian, H.B. (2011). An Intelligent Keyboard Framework for Improving Disabled People Computer Accessibility. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN AIAI 2011 2011. IFIP Advances in Information and Communication Technology, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23957-1_43
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DOI: https://doi.org/10.1007/978-3-642-23957-1_43
Publisher Name: Springer, Berlin, Heidelberg
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