Slant Classification Using FuzzySIS
This paper covers the area of signature recognition and fuzzy logic. It addresses the usage of fuzzy logic to be applied in signature slant recognition. Signature slant is vaguely identifiable and hard to determine for its slant form, in this study, the aim is to distinguish right slant, left slant...
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| creator | Shamsuddin, M.R. Ibrahim, Z. Mohamed, A. |
| description | This paper covers the area of signature recognition and fuzzy logic. It addresses the usage of fuzzy logic to be applied in signature slant recognition. Signature slant is vaguely identifiable and hard to determine for its slant form, in this study, the aim is to distinguish right slant, left slant and vertical slant. As fuzzy can deal with vague and ambiguous terms, it is applied to solve this problem. Based on 66 acquired signatures, the fuzzy range is identified. Then, a fuzzy slant identification system (FuzzySIS) is created. Identifiable slant degrees are extracted using a slant identification algorithm to yield an input for the created fuzzy systems. The result is then tested for its accuracy with an available 100 sample of proofed signatures. The result shows a favorable accuracy of 81% correct slant identification. It is hoped that implementation would be able to give some degree of contribution in the area of signature recognition and fuzzy logic. |
| doi_str_mv | 10.1109/ICCIT.2008.381 |
| format | Conference Proceeding |
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It addresses the usage of fuzzy logic to be applied in signature slant recognition. Signature slant is vaguely identifiable and hard to determine for its slant form, in this study, the aim is to distinguish right slant, left slant and vertical slant. As fuzzy can deal with vague and ambiguous terms, it is applied to solve this problem. Based on 66 acquired signatures, the fuzzy range is identified. Then, a fuzzy slant identification system (FuzzySIS) is created. Identifiable slant degrees are extracted using a slant identification algorithm to yield an input for the created fuzzy systems. The result is then tested for its accuracy with an available 100 sample of proofed signatures. The result shows a favorable accuracy of 81% correct slant identification. It is hoped that implementation would be able to give some degree of contribution in the area of signature recognition and fuzzy logic.</description><identifier>ISBN: 0769534074</identifier><identifier>ISBN: 9780769534077</identifier><identifier>DOI: 10.1109/ICCIT.2008.381</identifier><identifier>LCCN: 2008928439</identifier><language>eng</language><publisher>IEEE</publisher><subject>fuzzy ; Fuzzy logic ; Fuzzy set theory ; Fuzzy sets ; Fuzzy systems ; Humans ; Inference algorithms ; Information technology ; signature feature ; Size measurement ; slant ; slant identification ; Testing ; Writing</subject><ispartof>2008 Third International Conference on Convergence and Hybrid Information Technology, 2008, Vol.1, p.1080-1085</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/4682177$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,27930,54925</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4682177$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Shamsuddin, M.R.</creatorcontrib><creatorcontrib>Ibrahim, Z.</creatorcontrib><creatorcontrib>Mohamed, A.</creatorcontrib><title>Slant Classification Using FuzzySIS</title><title>2008 Third International Conference on Convergence and Hybrid Information Technology</title><addtitle>ICCIT</addtitle><description>This paper covers the area of signature recognition and fuzzy logic. It addresses the usage of fuzzy logic to be applied in signature slant recognition. Signature slant is vaguely identifiable and hard to determine for its slant form, in this study, the aim is to distinguish right slant, left slant and vertical slant. As fuzzy can deal with vague and ambiguous terms, it is applied to solve this problem. Based on 66 acquired signatures, the fuzzy range is identified. Then, a fuzzy slant identification system (FuzzySIS) is created. Identifiable slant degrees are extracted using a slant identification algorithm to yield an input for the created fuzzy systems. The result is then tested for its accuracy with an available 100 sample of proofed signatures. The result shows a favorable accuracy of 81% correct slant identification. It is hoped that implementation would be able to give some degree of contribution in the area of signature recognition and fuzzy logic.</description><subject>fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Humans</subject><subject>Inference algorithms</subject><subject>Information technology</subject><subject>signature feature</subject><subject>Size measurement</subject><subject>slant</subject><subject>slant identification</subject><subject>Testing</subject><subject>Writing</subject><isbn>0769534074</isbn><isbn>9780769534077</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjT1LA0EUABckoIlpbWwOrO987-3H2y1lMeYgYHFJHdbLrqycp2TPIvn1KloNTDEjxA1CgwjuvvW-3TYEYBtp8ULMgY3TUgGrmZj_ekdWSXcplqW8AQA6w8h4Je66IYxT5YdQSk65D1P-GKtdyeNrtfo6n09d212LWQpDict_LsRu9bj163rz_NT6h02dkfVUB0Ogf0aRsLcRkgGrNJHjBMlGkkyKDR-kTkFTL4kJDJjeRQcvxsFBLsTtXzfHGPefx_wejqe9MpaQWX4D0gw8vg</recordid><startdate>200811</startdate><enddate>200811</enddate><creator>Shamsuddin, M.R.</creator><creator>Ibrahim, Z.</creator><creator>Mohamed, A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200811</creationdate><title>Slant Classification Using FuzzySIS</title><author>Shamsuddin, M.R. ; Ibrahim, Z. ; Mohamed, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-a6205892e21c8e0f608452297f0f8e23724767d35fa52c32720606c9e90b690d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy set theory</topic><topic>Fuzzy sets</topic><topic>Fuzzy systems</topic><topic>Humans</topic><topic>Inference algorithms</topic><topic>Information technology</topic><topic>signature feature</topic><topic>Size measurement</topic><topic>slant</topic><topic>slant identification</topic><topic>Testing</topic><topic>Writing</topic><toplevel>online_resources</toplevel><creatorcontrib>Shamsuddin, M.R.</creatorcontrib><creatorcontrib>Ibrahim, Z.</creatorcontrib><creatorcontrib>Mohamed, A.</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>Shamsuddin, M.R.</au><au>Ibrahim, Z.</au><au>Mohamed, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Slant Classification Using FuzzySIS</atitle><btitle>2008 Third International Conference on Convergence and Hybrid Information Technology</btitle><stitle>ICCIT</stitle><date>2008-11</date><risdate>2008</risdate><volume>1</volume><spage>1080</spage><epage>1085</epage><pages>1080-1085</pages><isbn>0769534074</isbn><isbn>9780769534077</isbn><abstract>This paper covers the area of signature recognition and fuzzy logic. It addresses the usage of fuzzy logic to be applied in signature slant recognition. Signature slant is vaguely identifiable and hard to determine for its slant form, in this study, the aim is to distinguish right slant, left slant and vertical slant. As fuzzy can deal with vague and ambiguous terms, it is applied to solve this problem. Based on 66 acquired signatures, the fuzzy range is identified. Then, a fuzzy slant identification system (FuzzySIS) is created. Identifiable slant degrees are extracted using a slant identification algorithm to yield an input for the created fuzzy systems. The result is then tested for its accuracy with an available 100 sample of proofed signatures. The result shows a favorable accuracy of 81% correct slant identification. It is hoped that implementation would be able to give some degree of contribution in the area of signature recognition and fuzzy logic.</abstract><pub>IEEE</pub><doi>10.1109/ICCIT.2008.381</doi><tpages>6</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | fuzzy Fuzzy logic Fuzzy set theory Fuzzy sets Fuzzy systems Humans Inference algorithms Information technology signature feature Size measurement slant slant identification Testing Writing |
| title | Slant Classification Using FuzzySIS |
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