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Androidapplikation för digitalisering av formulär: Minimering av inlärningstid, kostnad och felsannolikhet
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
2018 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This study was performed by creating an android application that uses custom object recognition to scan and digitalize a series of checkbox form for example to correct multiple-choice questions or collect forms in a spreadsheet. The purpose with this study was to see which dataset and hardware with the machine learning library TensorFlow was cheapest, price worthy, enough reliable and fastest. A dataset of filled example forms with annotated checkboxes was created and used in the learning process. The model that was used for the object recognition was Single Show MultiBox Detector, MobileNet version, because it can detect multiple objects in the same image as well as it doesn’t have as high hardware requirements making it fitted for phones. The learning process was done in Google Clouds Machine Learning Engine with different image resolutions and cloud configurations. After the learning process on the cloud the finished TensorFlow model was converted to the TensorFlow Lite model that gets used in phones. The TensorFlow Lite model was used in the compilation of the android application so that the object recognition could work. The android application worked and could recognize the inputs in the checkbox form. Different image resolutions and cloud configurations during the learning process gave different results when it comes to which one was fastest and cheapest. In the end the conclusion was that Googles hardware setup STANDARD_1 was 20% faster than BASIC that was 91% cheaper and more price worthy with this dataset.

Abstract [sv]

Denna studie genomfördes genom att skapa en fungerande androidapplikation som använder sig av en anpassad objektigenkänning för att skanna och digitalisera en serie av kryssruteformulär exempelvis för att rätta flervalsfrågor eller sammanställa enkäter i ett kalkylark. Syftet med undersökningen var att se vilka datauppsättningar och hårdvara med maskininlärningsbiblioteket TensorFlow som var billigast, mest prisvärd, tillräcklig tillförlitlig och snabbast. En datauppsättning av ifyllda exempelformulär med klassificerade kryssrutor skapades och användes i inlärningsprocessen. Modellen som användes för objektigenkänningen blev Single Shot MultiBox Detector, version MobileNet, för att denna kan känna igen flera objekt i samma bild samt att den inte har lika höga hårdvarukrav vilket gör den anpassad för mobiltelefoner. Inlärningsprocessen utfördes i Google Clouds Machine Learning Engine med olika bildupplösningar och molnkonfiguration. Efter inlärningsprocessen på molnet konverterades den färdiga TensorFlow- modellen till en TensorFlow Lite-modell som används i mobiltelefoner. TensorFlow Lite-modellen användes i kompileringen av androidapplikationen för att objektigenkänningen skulle fungera. Androidapplikationen fungerade och kunde känna igen alla inmatningar i kryssruteformuläret. Olika bildupplösningar och molnkonfigurationer under inlärningsprocessen gav olika resultat när det gäller vilken som var snabbast eller billigast. I slutändan drogs slutsatsen att Googles hårdvaruuppsättning STANDARD_1 var 20% snabbare än BASIC som var 91% billigare och mest prisvärd med denna datauppsättning.

Place, publisher, year, edition, pages
2018. , p. 27
Keywords [en]
Machine learning, TensorFlow, object recognition, computer engineering
Keywords [sv]
Maskininlärning, TensorFlow, objektigenkänning, datateknik
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:miun:diva-35623Local ID: DT-H18-G3-003OAI: oai:DiVA.org:miun-35623DiVA, id: diva2:1288189
Subject / course
Computer Engineering DT1
Educational program
Master of Science in Engineering - Computer Engineering TDTEA 300 higher education credits
Supervisors
Examiners
Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-08-22Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • 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
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  • text
  • asciidoc
  • rtf