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<datestamp>2017-11-06</datestamp>
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<title lang=en>Haar-like-features for query-by-string word spotting</title>
<creator>Ghorbel, Adam</creator>
<creator>Ogier, Jean-Marc</creator>
<creator>Vincent, Nicole</creator>
<contributor>Laboratoire Informatique, Image et Interaction (L3I) ; Université de La Rochelle (ULR)</contributor>
<contributor>Laboratoire d'Informatique Paris Descartes (LIPADE - EA 2517) ; Université Paris Descartes - Paris 5 (UPD5)</contributor>
<description>International audience</description>
<source>17th Biennial Conference of the International Graphonomics Society</source>
<coverage>Pointe-à-Pitre, Guadeloupe</coverage>
<contributor>International Graphonomics Society (IGS)</contributor>
<contributor>Université des Antilles (UA)</contributor>
<contributor>Céline Rémi</contributor>
<contributor>Lionel Prévost</contributor>
<contributor>Eric Anquetil</contributor>
<identifier>hal-01165920</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165920</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165920/document</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165920/file/IGS_2015_submission_33.pdf</identifier>
<source>https://hal.univ-antilles.fr/hal-01165920</source>
<source>Céline Rémi; Lionel Prévost; Eric Anquetil. 17th Biennial Conference of the International Graphonomics Society, Jun 2015, Pointe-à-Pitre, Guadeloupe. 2015, Drawing, Handwriting Processing Analysis: New Advances and Challenges</source>
<language>en</language>
<subject lang=en>Haar-Like-Features</subject>
<subject lang=en>IAM Handwriting Database</subject>
<subject>[INFO] Computer Science [cs]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>This paper addresses the problem of word spotting in handwritten documents. The method is segmentation-free and follows the query-by-string paradigm. In the paper, we focus on the first step of the whole bio-inspired process that is based on two filtering steps, which are a global filtering followed by a more local filtering after a change of observation scale. The contribution of this approach is the use and the generalization of the Haar-Like-Features for the analysis of the document images, inspired from the famous visual perception principle. Different pieces of information are extracted from the whole image before drawing a conclusion, after a process of accumulation of votes. The method is evaluated using the IAM Handwriting Database.</description>
<date>2015-06-21</date>
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