prikaz prve stranice dokumenta Prepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja
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master's thesis
Prepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:677694
Džida, Mladen
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Džida, M. (2023). Prepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:677694

Džida, Mladen. "Prepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:677694

Džida, Mladen. "Prepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:677694

Džida, M. (2023). 'Prepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 21 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:677694

Džida M. Prepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 21] Available at: https://urn.nsk.hr/urn:nbn:hr:168:677694

M. Džida, "Prepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:677694

Metadata
TitlePrepoznavanje višealfabetnog teksta na slici korištenjem dubokog učenja
Title (english)Multi-alphabet Text Recognition Using Deep Learning
AuthorMladen Džida
MentorMarin Šilić (mentor)
Committee memberMarin Šilić (predsjednik povjerenstva)
Committee memberGoran Delač (član povjerenstva)
Committee memberKlemo Vladimir (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-04, Croatia
Abstract
U ovom radu je definirana arhitektura i provedena implementacija sustava za prepoznavanje višealfabetnog teksta na slici. Konkretno, opisani sustav je dizajniran za prepoznavanje latinice, arapskog i korejskog alfabeta. Sustav je implementiran u programskom jeziku Python, a za model je korišten vizualni transformer. Sa javnih internetskih stranica je prikupljen i pripremljen podatkovni skup za testiranje, a podatkovni skup za treniranje je umjetno generiran zbog nepostojanja dovoljno velikog javno dostupnog skupa. Model je, zbog ograničenih resursa, uspio samo u određenoj mjeri riješiti ovaj izazovni problem sa točnošću od 45.027%, ali glavni mu je doprinos to što predstavlja početnu točku za višealfabetno prepoznavanje teksta. Implementirani model ima mjesta za napredak, a najvažnija nadogradnja bi bila koristiti veći podatkovni skup za treniranje te isprobati veći skup metoda za pretprocesiranje slike. Trenirani su i modeli koji prepoznavaju zasebno svaki od tri navedena alfabeta. Modeli sa jednim alfabetom postižu bolje rezultate na tom individualnom alfabetu, ali se sami po sebi ne mogu koristiti kao sustav višealfabetnog prepoznavanja teksta jer im je potreban još jedan model koji će prvo odrediti o kojem se jeziku radi pa zatim proslijediti ulaznu sliku na odgovarajući model.
Abstract (english)
In this project, the architecture and the implementation of the system for recognizing multi-alphabet text on the image is carried out. In particular, the described system is designed to recognize Latin, Arabic and Korean alphabets. The system was implemented in the Python programming language, and a visual transformer was used for the model. A dataset for testing was collected and prepared from public websites, and a dataset for training was artificially generated due to the lack of a large enough publicly available datasets. The model, due to limited resources, only partially managed to solve this challenging problem with an accuracy of 45.027%, but its main contribution is that it represents a starting point for multi-alphabet text recognition. The implemented model has room for improvement, and the most important upgrades would be to use a larger training dataset and to try a larger set of image preprocessing methods. Models that recognize each of the three listed alphabets separately were also trained. Single-alphabet models perform better on that individual alphabet, but they by themselves cannot be used as a multi-alphabet text recognition system because they need another model to first determine what language is found on an image and then pass the input image to the appropriate model.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:677694
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
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Created on2023-10-11 08:46:52