Human-centered compression for efficient text input

Authors Rani Nelken, Stuart M. Shieber

Thumbnail PDF


  • Filesize: 98 kB
  • 2 pages

Document Identifiers

Author Details

Rani Nelken
Stuart M. Shieber

Cite AsGet BibTex

Rani Nelken and Stuart M. Shieber. Human-centered compression for efficient text input. In Efficient Text Entry. Dagstuhl Seminar Proceedings, Volume 5382, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)


Traditional methods for efficient text entry are based on prediction. Prediction requires a constant context-shift between entering text and selecting or verifying the predictions. Previous research has shown that the advantages offered by prediction are usually eliminated by the cognitive load associated with such context-switching. We present a novel approach that relies on compression. Users are required to compress text using a very simple abbreviation technique that yields an average keystrok reduction of 26.4%. Input text is automatically decoded using weighted finite-state transducers, incorporating both word-based and letter-based n-gram language models. Decoding yields a residual error rate of 3.3%. User experiments show that this approach yields improved text input speeds.
  • Prediction
  • compression
  • weigthed finite state transducers
  • text input


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail