Diagonal based feature extraction for handwritten character recognition system using neural network
An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 2...
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| creator | Pradeep, J. Srinivasan, E. Himavathi, S. |
| description | An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and twenty different handwritten alphabets characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names. |
| doi_str_mv | 10.1109/ICECTECH.2011.5941921 |
| format | Conference Proceeding |
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A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and twenty different handwritten alphabets characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.</description><subject>Accuracy</subject><subject>Artificial neural networks</subject><subject>Character recognition</subject><subject>Feature extraction</subject><subject>Feed forward propagation Neural Network</subject><subject>Handwriting recognition</subject><subject>Handwritten Character Recognition</subject><subject>Pixel</subject><subject>processing</subject><subject>Training</subject><isbn>1424486785</isbn><isbn>9781424486786</isbn><isbn>9781424486793</isbn><isbn>1424486793</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UMFKAzEUjIig1v0CEfIDrckmm-w7ylrbQsFLPZds8nYbbbOSpNT-vVXrXIZhhmEYQh44m3DO4HHRTJvVtJlPSsb5pALJoeQXpABdc1lKWSsN4pLc_ou6uiZFSu_sBKWg1OKG2Gdv-iGYLW1NQkc7NHkfkeJXjsZmPwTaDZFuTHCH6HPGQO3G_FgYaUQ79MH_ptIxZdzRffKhpwH38VQZMB-G-HFHrjqzTViceUTeXqarZj5evs4WzdNybE8L8xiEY3UrFTe8MghOQ90q4WSJxjkrsOOAGlgLSmBVgZWOawu6q1jLlVGtGJH7v16PiOvP6HcmHtfnX8Q39g5aEA</recordid><startdate>201104</startdate><enddate>201104</enddate><creator>Pradeep, J.</creator><creator>Srinivasan, E.</creator><creator>Himavathi, S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201104</creationdate><title>Diagonal based feature extraction for handwritten character recognition system using neural network</title><author>Pradeep, J. ; Srinivasan, E. ; Himavathi, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c142t-93d08b461a15ae9d798b63d42eaddc3ef19e790b963e559c4d17c97f50b16a6b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Accuracy</topic><topic>Artificial neural networks</topic><topic>Character recognition</topic><topic>Feature extraction</topic><topic>Feed forward propagation Neural Network</topic><topic>Handwriting recognition</topic><topic>Handwritten Character Recognition</topic><topic>Pixel</topic><topic>processing</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Pradeep, J.</creatorcontrib><creatorcontrib>Srinivasan, E.</creatorcontrib><creatorcontrib>Himavathi, S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pradeep, J.</au><au>Srinivasan, E.</au><au>Himavathi, S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Diagonal based feature extraction for handwritten character recognition system using neural network</atitle><btitle>2011 3rd International Conference on Electronics Computer Technology</btitle><stitle>ICECTECH</stitle><date>2011-04</date><risdate>2011</risdate><volume>4</volume><spage>364</spage><epage>368</epage><pages>364-368</pages><isbn>1424486785</isbn><isbn>9781424486786</isbn><eisbn>9781424486793</eisbn><eisbn>1424486793</eisbn><abstract>An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and twenty different handwritten alphabets characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.</abstract><pub>IEEE</pub><doi>10.1109/ICECTECH.2011.5941921</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Artificial neural networks Character recognition Feature extraction Feed forward propagation Neural Network Handwriting recognition Handwritten Character Recognition Pixel processing Training |
| title | Diagonal based feature extraction for handwritten character recognition system using neural network |
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