Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Gesture Keyboard USING MACHINE LEARNING
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The market for mobile devices is expanding rapidly. Input of text is a large part of using a mobile device and an input method that is convenient and fast is therefore very interesting. Gesture keyboards allow the user to input text by dragging a finger over the letters in the desired word. This study investigates if enhancements of gesture keyboards can be accomplished using machine learning. A gesture keyboard was developed based on an algorithm which used a Multilayer Perceptron with backpropagation and evaluated. The results indicate that the evaluated implementation is not an optimal solution to the problem of recognizing swiped words.

Abstract [sv]

Marknaden för mobila enheter expanderar kraftigt. Inmatning är en viktig del vid användningen av sådana produkter och en inmatningsmetod som är smidig och snabb är därför mycket intressant. Ett tangentbord för gester erbjuder användaren möjligheten att skriva genom att dra fingret över bokstäverna i det önskade ordet. I denna studie undersöks om tangentbord för gester kan förbättras med hjälp av maskininlärning. Ett tangentbord som använde en Multilayer Perceptron med backpropagation utvecklades och utvärderades. Resultaten visar att den undersökta implementationen inte är en optimal lösning på problemet att känna igen ord som matas in med hjälp av gester.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157141OAI: oai:DiVA.org:kth-157141DiVA: diva2:769351
Examiners
Available from: 2014-12-08 Created: 2014-12-08 Last updated: 2014-12-08Bibliographically approved

Open Access in DiVA

fulltext(746 kB)161 downloads
File information
File name FULLTEXT01.pdfFile size 746 kBChecksum SHA-512
0c256e872084e129b5f8d0ccc25407881cb080cd339fcc97bd3d13b61642b8b8f3c2b5db2fa7cf53da083d7497edcd75a92859af4d6d2a2167b6424c7285112d
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 161 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 108 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf