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Paper Abstract and Keywords
Presentation 2005-03-17 15:15
[Special Talk] Bayesian Inference in Document Understanding
Jin Hyung Kim (KAIST)
Abstract (in Japanese) (See Japanese page) 
(in English) Recently Bayesian approach gains popularity as basic inference engine for online and offline document understanding. It is applied for almost every step of document analysis and understanding, such as image enhancement, structure modeling, model-based matching, post-processing by stochastic language models, as well as multiple classifier combination. Such Bayesian approach is applied under the various frameworks such as Hidden Markov Model, Bayesian Network, Markov random field, etc.

Large portions of this talk will be devoted on Bayesian Network approach for online and offline character recognition with stochastic stroke analyses. Noticing the value of structure analysis in oriental character recognition, a unified probabilistic framework is developed for modeling shapes of constituent strokes and their relations. Such modeling scheme is a noble combination of structural analysis and statistical approach preserving advantages of both approaches. Utilizing point-stroke-character hierarchy and statistical dependencies in such structure, characters are modeled as a Bayesian Network which overcomes the crudeness of naïve Bayesian approach as well as the complexity of brute force Bayesian approach. Therefore, character shapes and stroke relationships are learnable from training data set, and the result of model analysis yields a probability value which can be used for further analysis and combined easily with other analysis results.

Briefly mentioned will be recent or on-going works at KAIST on various methodologies and innovative applications of document analysis. The methodologies include example-based Bayesian image enhancement by super-resolution, dependency-based multiple classifier combination, stochastic language modeling techniques from oriental language corpus, while the applications include historical document analysis for digital libraries and interface by writing in 3D space.
Keyword (in Japanese) (See Japanese page) 
(in English) Document analysis / Handwritten oriental character recognition / Bayesian network / Stroke shape analysis / Stroke relationship analysis / / /  
Reference Info. IEICE Tech. Rep., vol. 104, no. 741, PRMU2004-228, pp. 95-95, March 2005.
Paper # PRMU2004-228 
Date of Issue 2005-03-10 (TL, PRMU) 
ISSN Print edition: ISSN 0913-5685
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Conference Information
Committee PRMU TL  
Conference Date 2005-03-17 - 2005-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To PRMU 
Conference Code 2005-03-PRMU-TL 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Bayesian Inference in Document Understanding 
Sub Title (in English)  
Keyword(1) Document analysis  
Keyword(2) Handwritten oriental character recognition  
Keyword(3) Bayesian network  
Keyword(4) Stroke shape analysis  
Keyword(5) Stroke relationship analysis  
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1st Author's Name Jin Hyung Kim  
1st Author's Affiliation KAIST (KAIST)
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Speaker Author-1 
Date Time 2005-03-17 15:15:00 
Presentation Time 60 minutes 
Registration for PRMU 
Paper # TL2004-60, PRMU2004-228 
Volume (vol) vol.104 
Number (no) no.739(TL), no.741(PRMU) 
Page p.95 
#Pages
Date of Issue 2005-03-10 (TL, PRMU) 


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