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Handwritten digit recognition based on segmentation-free method
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2020 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis aims to implement a segmentation-free strategy in the context of handwritten multi-digit string recognition. Three models namely VGG-16, CRNN and 4C are built to be evaluated and benchmarked, also research about the effect of the different training set on model performance is carried out.
Place, publisher, year, edition, pages
2020. , p. 62
Keywords [en]
handwritten digit recognition, optical character recognition, segmentation-free
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-20685OAI: oai:DiVA.org:bth-20685DiVA, id: diva2:1499591
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVADA Master Qualification Plan in Computer Science
Supervisors
Examiners
Available from: 2020-11-10 Created: 2020-11-09 Last updated: 2020-11-10Bibliographically approved

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Handwritten digit recognition based on segmentation-free method(2589 kB)1996 downloads
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CiteExportLink to record
Permanent link

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Cite
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
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