DocumentCode :
2512060
Title :
A Probabilistic Language Model for Hand Drawings
Author :
Akce, Abdullah ; Bretl, Timothy
Author_Institution :
Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
109
Lastpage :
112
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 electroencephalograph (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.
Keywords :
Markov processes; dynamic programming; natural language interfaces; natural language processing; probability; visual databases; EEG; Markov model; artwork database; binary command; dynamic programming; electroencephalograph; hand drawings; low-bandwidth input devices; low-cardinality set; natural language processing; optical character recognition; probabilistic language model; scale parameter; speech recognition; text entry interfaces; Approximation methods; Brain modeling; Computational modeling; Dynamic programming; Entropy; Markov processes; Probabilistic logic; Brain-computer interface; graphics recognition; human body motion and gesture based interaction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.35
Filename :
5597640
Link To Document :
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