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Classifying Receipts and Invoices in Visma Mobile Scanner
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
2016 (engelsk)Independent thesis Basic level (degree of Bachelor), 20 poäng / 30 hpOppgave
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.

sted, utgiver, år, opplag, sider
2016. , s. 24
Emneord [en]
Machine Learning, classifying, OCR, Tesseract, Fine Reader
HSV kategori
Identifikatorer
URN: urn:nbn:se:lnu:diva-49671OAI: oai:DiVA.org:lnu-49671DiVA, id: diva2:901992
Eksternt samarbeid
Visma
Fag / kurs
Computer Science
Utdanningsprogram
Software Technology Programme, 180 credits
Veileder
Examiner
Tilgjengelig fra: 2016-02-10 Laget: 2016-02-09 Sist oppdatert: 2016-02-10bibliografisk kontrollert

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