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A Multimodal Approach to Autonomous Document Categorization Using Convolutional Neural Networks
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

When international students apply for the Swedish educational system, they send documents to verify their merits. These documents are categorized and evaluated by administrators. This thesis approach the problem of document classification with a multimodal convolutional network. By looking at both image and text features together, it is examined if the classification is better than any of the sources alone. The best result for single source classification was when the input was text at 85.2% accuracy, this was topped by the multimodal approach with a accuracy of 88.4%.This thesis concludes that there is a gain in accuracy when using a multimodal approach.

Place, publisher, year, edition, pages
2018. , p. 24
Series
UMNAD ; 1176
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-156289OAI: oai:DiVA.org:umu-156289DiVA, id: diva2:1287836
External cooperation
ITS
Educational program
Bachelor of Science Programme in Computing Science
Supervisors
Examiners
Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-02-12Bibliographically approved

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fulltext(388 kB)104 downloads
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf