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Empirical Evaluation of Approaches for Digit Recognition
Linnaeus University, Faculty of Technology, Department of Computer Science.
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Optical Character Recognition (OCR) is a well studied subject involving variousapplication areas. OCR results in various limited problem areas are promising,however building highly accurate OCR application is still problematic in practice.This thesis discusses the problem of recognizing and confirming Bingo lottery numbersfrom a real lottery field, and a prototype for Android phone is implementedand evaluated. An OCR library Tesseract and two Artificial Neural Network (ANN)approaches are compared in an experiment and discussed. The results show thattraining a neural network for each number gives slightly higher results than Tesseract.

Place, publisher, year, edition, pages
2015. , 19 p.
Keyword [en]
Optical Character Recognition, OCR, Artificial Neural Network, ANN
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-46676OAI: oai:DiVA.org:lnu-46676DiVA: diva2:859696
Subject / course
Computer Science
Supervisors
Examiners
Available from: 2015-10-09 Created: 2015-10-08 Last updated: 2018-01-11Bibliographically approved

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fulltext(1941 kB)1425 downloads
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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More languages
Output format
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