Maximization of mutual information for offline Thai handwriting recognition
This paper aims to improve the performance of an HMM-based offline Thai handwriting recognition system through discriminative training and the use of fine-tuned feature extraction methods. The discriminative training is implemented by maximizing the mutual information between the data and their clas...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2006-08, Vol.28 (8), p.1347-1351 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Nopsuwanchai, R. Biem, A. Clocksin, W.F. |
| description | This paper aims to improve the performance of an HMM-based offline Thai handwriting recognition system through discriminative training and the use of fine-tuned feature extraction methods. The discriminative training is implemented by maximizing the mutual information between the data and their classes. The feature extraction is based on our proposed block-based PCA and composite images, shown to be better at discriminating Thai confusable characters. We demonstrate significant improvements in recognition accuracies compared to the classifiers that are not discriminatively optimized |
| doi_str_mv | 10.1109/TPAMI.2006.167 |
| format | Article |
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The discriminative training is implemented by maximizing the mutual information between the data and their classes. The feature extraction is based on our proposed block-based PCA and composite images, shown to be better at discriminating Thai confusable characters. We demonstrate significant improvements in recognition accuracies compared to the classifiers that are not discriminatively optimized</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>EISSN: 2160-9292</identifier><identifier>DOI: 10.1109/TPAMI.2006.167</identifier><identifier>PMID: 16886869</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>Algorithms ; Applied sciences ; Artificial Intelligence ; Automatic Data Processing - methods ; Blocking ; Character recognition ; Clocks ; Computer science; control theory; systems ; Computer Simulation ; discriminative training ; Documentation - methods ; Exact sciences and technology ; Feature extraction ; Handwriting ; Handwriting recognition ; Hidden Markov Model ; Hidden Markov models ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Information Storage and Retrieval - methods ; Intelligence ; Likelihood Functions ; Maximization ; Maximum likelihood estimation ; Models, Statistical ; Mutual information ; Online Systems ; Optical character recognition software ; Optimization methods ; Pattern analysis ; Pattern Recognition, Automated - methods ; Pattern recognition. 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The discriminative training is implemented by maximizing the mutual information between the data and their classes. The feature extraction is based on our proposed block-based PCA and composite images, shown to be better at discriminating Thai confusable characters. We demonstrate significant improvements in recognition accuracies compared to the classifiers that are not discriminatively optimized</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Automatic Data Processing - methods</subject><subject>Blocking</subject><subject>Character recognition</subject><subject>Clocks</subject><subject>Computer science; control theory; systems</subject><subject>Computer Simulation</subject><subject>discriminative training</subject><subject>Documentation - methods</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Handwriting</subject><subject>Handwriting recognition</subject><subject>Hidden Markov Model</subject><subject>Hidden Markov models</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Information Storage and Retrieval - methods</subject><subject>Intelligence</subject><subject>Likelihood Functions</subject><subject>Maximization</subject><subject>Maximum likelihood estimation</subject><subject>Models, Statistical</subject><subject>Mutual information</subject><subject>Online Systems</subject><subject>Optical character recognition software</subject><subject>Optimization methods</subject><subject>Pattern analysis</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>PCA</subject><subject>Principal component analysis</subject><subject>Thai handwriting recognition</subject><subject>Thailand</subject><subject>Training</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0c1vFCEYBnBiNHatXr2YmImJepr1BYYXODaNH41t9LCeydtZaGlmmAoz8eOvl3U3qfGgJwj8eAI8jD3lsOYc7JvN55OLs7UAwDVHfY-tuJW2lUra-2wFHEVrjDBH7FEpNwC8UyAfsiOOxqBBu2IfL-h7HONPmuOUmik04zIvNDQxhSmP-9U6qzthiMk3m2uKzTWl7bcc55iumuz76SrFHXzMHgQain9yGI_Zl3dvN6cf2vNP789OT87bvpNybjsSwJUHSySQi3qRPliUgJI6r5H0lpsQEDsfSKNCCKAurbcCpFZ1Rx6z1_vc2zx9XXyZ3RhL74eBkp-W4oxFIQQYXeWrf0o0un4iV_-FwoCSGrHCF3_Bm2nJqT7XGVRadiB2aes96vNUSvbB3eY4Uv7hOLhdbe53bW5Xm6u11QPPD6nL5ei3d_zQUwUvD4BKT0PIlPpY7py2UnFuqnu2d9F7_0dMJxCN_AXJ26cz</recordid><startdate>20060801</startdate><enddate>20060801</enddate><creator>Nopsuwanchai, R.</creator><creator>Biem, A.</creator><creator>Clocksin, W.F.</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Digital image processing. Computational geometry</topic><topic>PCA</topic><topic>Principal component analysis</topic><topic>Thai handwriting recognition</topic><topic>Thailand</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nopsuwanchai, R.</creatorcontrib><creatorcontrib>Biem, A.</creatorcontrib><creatorcontrib>Clocksin, W.F.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Nopsuwanchai, R.</au><au>Biem, A.</au><au>Clocksin, W.F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Maximization of mutual information for offline Thai handwriting recognition</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2006-08-01</date><risdate>2006</risdate><volume>28</volume><issue>8</issue><spage>1347</spage><epage>1351</epage><pages>1347-1351</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>This paper aims to improve the performance of an HMM-based offline Thai handwriting recognition system through discriminative training and the use of fine-tuned feature extraction methods. The discriminative training is implemented by maximizing the mutual information between the data and their classes. The feature extraction is based on our proposed block-based PCA and composite images, shown to be better at discriminating Thai confusable characters. We demonstrate significant improvements in recognition accuracies compared to the classifiers that are not discriminatively optimized</abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><pmid>16886869</pmid><doi>10.1109/TPAMI.2006.167</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Applied sciences Artificial Intelligence Automatic Data Processing - methods Blocking Character recognition Clocks Computer science control theory systems Computer Simulation discriminative training Documentation - methods Exact sciences and technology Feature extraction Handwriting Handwriting recognition Hidden Markov Model Hidden Markov models Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Information Storage and Retrieval - methods Intelligence Likelihood Functions Maximization Maximum likelihood estimation Models, Statistical Mutual information Online Systems Optical character recognition software Optimization methods Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry PCA Principal component analysis Thai handwriting recognition Thailand Training |
| title | Maximization of mutual information for offline Thai handwriting recognition |
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