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A Neural Network Approach to Arbitrary SymbolRecognition on Modern Smartphones
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Making a computer understand handwritten text and symbols have numerous applications ranging from reading of bank checks, mail addresses or digitalizing arbitrary note taking. Benefits include atomization of processes, efficient electronic storage, and possible augmented usage of parsed content. This report will provide an overview of how off-line handwriting recognition systems can be constructed. We will show how such systems can be split into isolated modules which can be constructed individually. Focus will be on handwritten single symbol recognition and we will present how this could be accomplished using convolutional neural networks on a modern smartphone. A symbol recognition prototype application for the Apple iOS operation system will be constructed and evaluated as a proof-of-concept. Results obtained during this project shows that it is possible to train a classifier to understand arbitrary symbols without the need to manually crafting class separating features and instead rely on deep learning for automatic structure discovery.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-153680OAI: oai:DiVA.org:kth-153680DiVA: diva2:753279
Examiners
Available from: 2014-11-21 Created: 2014-10-07 Last updated: 2014-11-21Bibliographically approved

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fulltext(3652 kB)346 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • nn-NB
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  • Other locale
More languages
Output format
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