dc.contributor.author | Riba, P | |
dc.contributor.author | Dutta, A | |
dc.contributor.author | Dey, S | |
dc.contributor.author | Llados, J | |
dc.contributor.author | Fornes, A | |
dc.date.accessioned | 2019-10-15T11:51:49Z | |
dc.date.issued | 2018-01-29 | |
dc.description.abstract | Information Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the state-of-The-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario. | en_GB |
dc.description.sponsorship | European Union Horizon 2020 | en_GB |
dc.description.sponsorship | Ministerio de Educación, Cultura y Deporte, Spain | en_GB |
dc.description.sponsorship | FPU | en_GB |
dc.description.sponsorship | CERCA Programme/Generalitat de Catalunya | en_GB |
dc.identifier.citation | 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 9-15 November 2017, Kyoto, Japan, pp. 475-480 | en_GB |
dc.identifier.doi | 10.1109/ICDAR.2017.84 | |
dc.identifier.grantnumber | 665919 | en_GB |
dc.identifier.grantnumber | TIN2015-70924-C2-2-R | en_GB |
dc.identifier.grantnumber | FPU15/06264 | en_GB |
dc.identifier.grantnumber | RYC-2014-16831 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/39213 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2018 IEEE | en_GB |
dc.subject | Databases | en_GB |
dc.subject | Image edge detection | en_GB |
dc.subject | Information retrieval | en_GB |
dc.subject | Manifolds | en_GB |
dc.subject | Shape | en_GB |
dc.subject | Visualization | en_GB |
dc.subject | Text analysis | en_GB |
dc.title | Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2019-10-15T11:51:49Z | |
dc.identifier.isbn | 9781538635865 | |
dc.identifier.issn | 1520-5363 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2018-01-29 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2019-10-15T11:49:20Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-10-15T11:51:52Z | |
refterms.panel | B | en_GB |