Biometric Signature Processing & Recognition Using Radial Basis Function Network
Automatic recognition of signature is a challenging problem which has received much attention during recent years due to its many applications in different fields. Signature has been used for long time for verification and authentication purpose. Earlier methods were manual but nowadays they are get...
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| creator | Chadha, Ankit Satam, Neha Wali, Vibha |
| description | Automatic recognition of signature is a challenging problem which has
received much attention during recent years due to its many applications in
different fields. Signature has been used for long time for verification and
authentication purpose. Earlier methods were manual but nowadays they are
getting digitized. This paper provides an efficient method to signature
recognition using Radial Basis Function Network. The network is trained with
sample images in database. Feature extraction is performed before using them
for training. For testing purpose, an image is made to undergo
rotation-translation-scaling correction and then given to network. The network
successfully identifies the original image and gives correct output for stored
database images also. The method provides recognition rate of approximately 80%
for 200 samples. |
| doi_str_mv | 10.48550/arxiv.1311.1694 |
| format | Article |
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received much attention during recent years due to its many applications in
different fields. Signature has been used for long time for verification and
authentication purpose. Earlier methods were manual but nowadays they are
getting digitized. This paper provides an efficient method to signature
recognition using Radial Basis Function Network. The network is trained with
sample images in database. Feature extraction is performed before using them
for training. For testing purpose, an image is made to undergo
rotation-translation-scaling correction and then given to network. The network
successfully identifies the original image and gives correct output for stored
database images also. The method provides recognition rate of approximately 80%
for 200 samples.</description><identifier>DOI: 10.48550/arxiv.1311.1694</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2013-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1311.1694$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1311.1694$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Chadha, Ankit</creatorcontrib><creatorcontrib>Satam, Neha</creatorcontrib><creatorcontrib>Wali, Vibha</creatorcontrib><title>Biometric Signature Processing & Recognition Using Radial Basis Function Network</title><description>Automatic recognition of signature is a challenging problem which has
received much attention during recent years due to its many applications in
different fields. Signature has been used for long time for verification and
authentication purpose. Earlier methods were manual but nowadays they are
getting digitized. This paper provides an efficient method to signature
recognition using Radial Basis Function Network. The network is trained with
sample images in database. Feature extraction is performed before using them
for training. For testing purpose, an image is made to undergo
rotation-translation-scaling correction and then given to network. The network
successfully identifies the original image and gives correct output for stored
database images also. The method provides recognition rate of approximately 80%
for 200 samples.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotjztPwzAUhb0woMLOhDyxNdjxI_ZIKwpIFVSlzNGtY0dXtDayUx7_njawnCN9RzrSR8gVZ5U0SrFbyN_4WXHBecW1ledkNcO090NGR1-xjzAcsqernJwvBWNPb-jau9RHHDBF-jayNXQIOzqDgoUuDtGN27MfvlJ-vyBnAXbFX_73hGwW95v543T58vA0v1tOQSt5DMN0x1y9VUY6CI1x0tfB26AsM6ZxNQgrj0A3wdVcb7tgvNaScQsmcCEm5PrvdjRqPzLuIf-0J7P2ZCZ-AcxuSS0</recordid><startdate>20131107</startdate><enddate>20131107</enddate><creator>Chadha, Ankit</creator><creator>Satam, Neha</creator><creator>Wali, Vibha</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20131107</creationdate><title>Biometric Signature Processing & Recognition Using Radial Basis Function Network</title><author>Chadha, Ankit ; Satam, Neha ; Wali, Vibha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a654-a6806d0c2b584caf78c4e2fe9f590887c2a3942fe67fc216bdf8e664019a8f133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Chadha, Ankit</creatorcontrib><creatorcontrib>Satam, Neha</creatorcontrib><creatorcontrib>Wali, Vibha</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chadha, Ankit</au><au>Satam, Neha</au><au>Wali, Vibha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Biometric Signature Processing & Recognition Using Radial Basis Function Network</atitle><date>2013-11-07</date><risdate>2013</risdate><abstract>Automatic recognition of signature is a challenging problem which has
received much attention during recent years due to its many applications in
different fields. Signature has been used for long time for verification and
authentication purpose. Earlier methods were manual but nowadays they are
getting digitized. This paper provides an efficient method to signature
recognition using Radial Basis Function Network. The network is trained with
sample images in database. Feature extraction is performed before using them
for training. For testing purpose, an image is made to undergo
rotation-translation-scaling correction and then given to network. The network
successfully identifies the original image and gives correct output for stored
database images also. The method provides recognition rate of approximately 80%
for 200 samples.</abstract><doi>10.48550/arxiv.1311.1694</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | Biometric Signature Processing & Recognition Using Radial Basis Function Network |
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