A probabilistic language model for hand drawings

Abdullah Akce, Timothy Bretl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Probabilistic language models are critical to applications in natural language processing that include speech recognition, optical character recognition, and interfaces for text entry. In this paper, we present a systematic way to learn a similar type of probabilistic language model for hand drawings from a database of existing artwork by representing each stroke as a sequence of symbols. First, we propose a language in which the symbols are circular arcs with length fixed by a scale parameter and with curvature chosen from a fixed low-cardinality set. Then, we apply an algorithm based on dynamic programming to represent each stroke of the drawing as a sequence of symbols from our alphabet. Finally, we learn the probabilistic language model by constructing a Markov model. We compute the entropy of our language in a test set as measured by the expected number of bits required for each symbol. Our language model might be applied in future work to create a drawing interface for noisy and low-bandwidth input devices, for example an electroen-cephalograph (EEG) that admits one binary command per second. The results indicate that by leveraging our language model, the performance of such an interface would be enhanced by about 20 percent.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages109-112
Number of pages4
DOIs
StatePublished - Nov 18 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period8/23/108/26/10

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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