prikaz prve stranice dokumenta Prepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela
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master's thesis
Prepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela
2018. urn:nbn:hr:168:550048
Kopljar, Damir
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Kopljar, D. (2018). Prepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:550048

Kopljar, Damir. "Prepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:550048

Kopljar, Damir. "Prepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:550048

Kopljar, D. (2018). 'Prepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 13 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:550048

Kopljar D. Prepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 April 13] Available at: https://urn.nsk.hr/urn:nbn:hr:168:550048

D. Kopljar, "Prepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:550048

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Metadata
TitlePrepoznavanje rukom pisanih identifikacijskih brojeva studenata uporabom dubokih modela
Title (english)Recognition of Handwritten Student Identification Numbers Based on Deep Models
AuthorDamir Kopljar
MentorMarko Čupić (mentor)
Committee memberMarko Čupić (predsjednik povjerenstva)
Committee memberŽeljka Mihajlović (član povjerenstva)
Committee memberAlan Jović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovome radu opisane su osnovne značajke konvolucijskih neuronskih mreža. Zatim je detaljno opisan rad modela za detekciju objekata. U radu su opisana i dva korištena podatkovna skupa, jedan prikupljen u skupu rada i drugi umjetno generiran. Također objašnjena je i korištena programska biblioteka TensorFlow Object Detection kojom su izrađeni svi modeli u radu. Na samome kraju opisani su i prikazani dobiveni rezultati.
Abstract (english)
This work describes the basic features of convolution neural networks. This work then describes features of object detection model. In this work two datasets were used and described, one that was collected from students on the university and other that was made artificially. This work also describes TensorFlow Object Detection framework which was used to build all models in this work. In the final part of the work, the results are shown.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:550048
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2019-03-13 21:42:32