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Zoning invariant holistic recognizer for hybrid recognition of handwriting

The paper describes a holistic recognizer developed for use in a hybrid recognition system. The recognizer uses information about the word shape. As this information is strongly related to word zoning, care is taken to avoid limitations resulting from the inaccuracy of zone detection. The recognizer...

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Main Authors:	Powalka, R.K., Sherkat, N., Whitrow, R.J.
Format:	Conference Proceeding
Language:	English
Subjects:	Bars Data mining Encoding Fuzzy sets Handwriting recognition Pattern matching Pattern recognition Robustness Shape Writing
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container_end_page	67 vol.1
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container_start_page	64
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creator	Powalka, R.K. Sherkat, N. Whitrow, R.J.
description	The paper describes a holistic recognizer developed for use in a hybrid recognition system. The recognizer uses information about the word shape. As this information is strongly related to word zoning, care is taken to avoid limitations resulting from the inaccuracy of zone detection. The recognizer uses a very simple set of features and a fuzzy set based pattern matching technique. This aims to increase its robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. The letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. The holistic recognizer is found capable of outperforming the segmentation based one, despite the remaining disambiguation problems. When working together in a hybrid system, the results are significantly higher than those of the individual recognizers. Recognition results are reported and compared.
doi_str_mv	10.1109/ICDAR.1995.598945
format	conference_proceeding
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The recognizer uses information about the word shape. As this information is strongly related to word zoning, care is taken to avoid limitations resulting from the inaccuracy of zone detection. The recognizer uses a very simple set of features and a fuzzy set based pattern matching technique. This aims to increase its robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. The letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. The holistic recognizer is found capable of outperforming the segmentation based one, despite the remaining disambiguation problems. When working together in a hybrid system, the results are significantly higher than those of the individual recognizers. Recognition results are reported and compared.</description><identifier>ISBN: 0818671289</identifier><identifier>ISBN: 9780818671289</identifier><identifier>DOI: 10.1109/ICDAR.1995.598945</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bars ; Data mining ; Encoding ; Fuzzy sets ; Handwriting recognition ; Pattern matching ; Pattern recognition ; Robustness ; Shape ; Writing</subject><ispartof>Proceedings of 3rd International Conference on Document Analysis and Recognition, 1995, Vol.1, p.64-67 vol.1</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/598945$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/598945$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Powalka, R.K.</creatorcontrib><creatorcontrib>Sherkat, N.</creatorcontrib><creatorcontrib>Whitrow, R.J.</creatorcontrib><title>Zoning invariant holistic recognizer for hybrid recognition of handwriting</title><title>Proceedings of 3rd International Conference on Document Analysis and Recognition</title><addtitle>ICDAR</addtitle><description>The paper describes a holistic recognizer developed for use in a hybrid recognition system. The recognizer uses information about the word shape. As this information is strongly related to word zoning, care is taken to avoid limitations resulting from the inaccuracy of zone detection. The recognizer uses a very simple set of features and a fuzzy set based pattern matching technique. This aims to increase its robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. The letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. The holistic recognizer is found capable of outperforming the segmentation based one, despite the remaining disambiguation problems. When working together in a hybrid system, the results are significantly higher than those of the individual recognizers. Recognition results are reported and compared.</description><subject>Bars</subject><subject>Data mining</subject><subject>Encoding</subject><subject>Fuzzy sets</subject><subject>Handwriting recognition</subject><subject>Pattern matching</subject><subject>Pattern recognition</subject><subject>Robustness</subject><subject>Shape</subject><subject>Writing</subject><isbn>0818671289</isbn><isbn>9780818671289</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1995</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNp9jr0OgjAYRZsYE_94AJ36AmILFNvRoEYdjZMLqVDgM9ialmjw6SVRV-9yc-5ZLkJTSnxKiVjsk_Xq6FMhmM8EFxHroRHhlMdLGnAxQJ5zV9IlYjSMgyE6nI0GXWLQD2lB6gZXpgbXQIatykyp4aUsLozFVXuxkP_WBozGpsCV1PnTdqjLCeoXsnbK-_YYzbabU7Kbg1IqvVu4Sdumn1fhX_kGW8I9Xw</recordid><startdate>1995</startdate><enddate>1995</enddate><creator>Powalka, R.K.</creator><creator>Sherkat, N.</creator><creator>Whitrow, R.J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1995</creationdate><title>Zoning invariant holistic recognizer for hybrid recognition of handwriting</title><author>Powalka, R.K. ; Sherkat, N. ; Whitrow, R.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_5989453</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Bars</topic><topic>Data mining</topic><topic>Encoding</topic><topic>Fuzzy sets</topic><topic>Handwriting recognition</topic><topic>Pattern matching</topic><topic>Pattern recognition</topic><topic>Robustness</topic><topic>Shape</topic><topic>Writing</topic><toplevel>online_resources</toplevel><creatorcontrib>Powalka, R.K.</creatorcontrib><creatorcontrib>Sherkat, N.</creatorcontrib><creatorcontrib>Whitrow, R.J.</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>Powalka, R.K.</au><au>Sherkat, N.</au><au>Whitrow, R.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Zoning invariant holistic recognizer for hybrid recognition of handwriting</atitle><btitle>Proceedings of 3rd International Conference on Document Analysis and Recognition</btitle><stitle>ICDAR</stitle><date>1995</date><risdate>1995</risdate><volume>1</volume><spage>64</spage><epage>67 vol.1</epage><pages>64-67 vol.1</pages><isbn>0818671289</isbn><isbn>9780818671289</isbn><abstract>The paper describes a holistic recognizer developed for use in a hybrid recognition system. The recognizer uses information about the word shape. As this information is strongly related to word zoning, care is taken to avoid limitations resulting from the inaccuracy of zone detection. The recognizer uses a very simple set of features and a fuzzy set based pattern matching technique. This aims to increase its robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. The letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. The holistic recognizer is found capable of outperforming the segmentation based one, despite the remaining disambiguation problems. When working together in a hybrid system, the results are significantly higher than those of the individual recognizers. Recognition results are reported and compared.</abstract><pub>IEEE</pub><doi>10.1109/ICDAR.1995.598945</doi></addata></record>
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Bars Data mining Encoding Fuzzy sets Handwriting recognition Pattern matching Pattern recognition Robustness Shape Writing
title	Zoning invariant holistic recognizer for hybrid recognition of handwriting
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