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Classifying Receipts and Invoices in Visma Mobile Scanner
Linnaeus University, Faculty of Technology, Department of Computer Science.
2016 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This paper presents a study on classifying receipts and invoices using Machine Learning. Furthermore, Naïve Bayes Algorithm and the advantages of using it will be discussed.  With information gathered from theory and previous research, I will show how to classify images into a receipt or an invoice. Also, it includes pre-processing images using a variety of pre-processing methods and text extraction using Optical Character Recognition (OCR). Moreover, the necessity of pre-processing images to reach a higher accuracy will be discussed. A result shows a comparison between Tesseract OCR engine and FineReader OCR engine. After embracing much knowledge from theory and discussion, the results showed that combining FineReader OCR engine and Machine Learning is increasing the accuracy of the image classification.

Place, publisher, year, edition, pages
2016. , 24 p.
Keyword [en]
Machine Learning, classifying, OCR, Tesseract, Fine Reader
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:lnu:diva-49671OAI: oai:DiVA.org:lnu-49671DiVA: diva2:901992
External cooperation
Visma
Subject / course
Computer Science
Educational program
Software Technology Programme, 180 credits
Supervisors
Examiners
Available from: 2016-02-10 Created: 2016-02-09 Last updated: 2016-02-10Bibliographically approved

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Type fulltextMimetype application/pdf

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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