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master's thesis
Optičko prepoznavanje rukopisa – usporedna analiza odabranih programskih rješenja
Zagreb: University of Zagreb, Faculty of Humanities and Social Sciences, 2024. urn:nbn:hr:131:122892
Sumpor, Josipa
University of Zagreb
Faculty of Humanities and Social Sciences
Department of information and Communication sciences

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Sumpor, J. (2024). Optičko prepoznavanje rukopisa – usporedna analiza odabranih programskih rješenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Humanities and Social Sciences. Retrieved from https://urn.nsk.hr/urn:nbn:hr:131:122892

Sumpor, Josipa. "Optičko prepoznavanje rukopisa – usporedna analiza odabranih programskih rješenja." Master's thesis, University of Zagreb, Faculty of Humanities and Social Sciences, 2024. https://urn.nsk.hr/urn:nbn:hr:131:122892

Sumpor, Josipa. "Optičko prepoznavanje rukopisa – usporedna analiza odabranih programskih rješenja." Master's thesis, University of Zagreb, Faculty of Humanities and Social Sciences, 2024. https://urn.nsk.hr/urn:nbn:hr:131:122892

Sumpor, J. (2024). 'Optičko prepoznavanje rukopisa – usporedna analiza odabranih programskih rješenja', Master's thesis, University of Zagreb, Faculty of Humanities and Social Sciences, accessed 15 August 2024, https://urn.nsk.hr/urn:nbn:hr:131:122892

Sumpor J. Optičko prepoznavanje rukopisa – usporedna analiza odabranih programskih rješenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Humanities and Social Sciences; 2024 [cited 2024 August 15] Available at: https://urn.nsk.hr/urn:nbn:hr:131:122892

J. Sumpor, "Optičko prepoznavanje rukopisa – usporedna analiza odabranih programskih rješenja", Master's thesis, University of Zagreb, Faculty of Humanities and Social Sciences, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:131:122892

Metadata
TitleOptičko prepoznavanje rukopisa – usporedna analiza odabranih programskih rješenja
Title (english)Handwritten text recognition – comparative analysis of selected software solutions
AuthorJosipa Sumpor
MentorHrvoje Stančić (mentor)
MentorŽeljko Trbušić (pomoć pri izradi, neposredni voditelj)
Committee memberArian Rajh (predsjednik povjerenstva)
Committee memberŽeljko Trbušić (član povjerenstva)
Committee memberHrvoje Stančić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Humanities and Social Sciences
(Department of information and Communication sciences)
Zagreb
Defense date and country2024-07-01, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Information and Communication Sciences
Archives and Documentation Studies
Abstract
U ovom diplomskom radu istražuju se programska rješenja za optičko prepoznavanje rukopisa, s posebnim naglaskom na usporedbu rezultata platforme Transkribus i mobilnih aplikacija. Cilj je procijeniti točnost i pouzdanost različitih tehnologija za prepoznavanje rukopisa. Analizirane su mobilne aplikacije OneNote, MyScript Nebo i Pen to Print, koje su se pokazale neučinkovitima. Kao napredna platforma, Transkribus nudi mogućnost treniranja vlastitih modela i podržava prepoznavanje velikog broja jezika i stilova rukopisa, dok mobilne aplikacije koriste jednostavnije algoritme i pružaju manje opcija za prilagodbu. Zbog ograničenja mobilnih aplikacija za prepoznavanje rukopisa, istraživanje se preusmjerilo na digitalizaciju gradiva I. klasične gimnazije u Zagrebu i treniranje vlastitog modela na Transkribusu. Digitalizacija je obuhvatila skeniranje i obradu 419 stranica, od kojih je 198 odabrano za daljnju izradu modela za prepoznavanje rukopisa. Provedena je, također, neovisna evaluacija rezultata koristeći ISRI evaluacijske alate.
Abstract (english)
This master thesis explores software solutions for handwritten text recognition, with a particular emphasis on comparing the results of the Transkribus platform and mobile applications. The aim is to assess the accuracy and reliability of various handwriting recognition technologies. Mobile applications OneNote, MyScript Nebo, and Pen to Print were analyzed and found to be ineffective. Transkribus, as an advanced platform, offers the capability to train custom models and supports recognition across a wide range of languages and handwriting styles, whereas mobile applications employ simpler algorithms and provide fewer customization options. Due to the limitations of mobile handwriting recognition applications, the research shifted focus to digitizing materials from the First Classical Gymnasium in Zagreb and training a custom Transkribus model. The digitization process involved scanning and processing 419 pages, of which 198 were selected for further model development for handwriting recognition. Additionally, an independent evaluation of the results was conducted using ISRI evaluation tools.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:131:122892
Study programmeTitle: Information Sciences; specializations in: Archivistics Study, Library Science, Information Sciences (research), Information Sciences (teaching), Museology and heritage management, Informatology
Course: Archivistics Study
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra informacijskih znanosti (magistar/magistra informacijskih znanosti)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to authenticated users
Terms of useLicence In copyright icon
Created on2024-07-12 20:21:17