prikaz prve stranice dokumenta Prepoznavanje skeniranog teksta temeljeno na dubokim modelima
No public access
master's thesis
Prepoznavanje skeniranog teksta temeljeno na dubokim modelima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:319432
Ambrošić, Dan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Ambrošić, D. (2019). Prepoznavanje skeniranog teksta temeljeno na dubokim modelima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:319432

Ambrošić, Dan. "Prepoznavanje skeniranog teksta temeljeno na dubokim modelima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:319432

Ambrošić, Dan. "Prepoznavanje skeniranog teksta temeljeno na dubokim modelima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:319432

Ambrošić, D. (2019). 'Prepoznavanje skeniranog teksta temeljeno na dubokim modelima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:319432

Ambrošić D. Prepoznavanje skeniranog teksta temeljeno na dubokim modelima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 December 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:319432

D. Ambrošić, "Prepoznavanje skeniranog teksta temeljeno na dubokim modelima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:319432

Metadata
TitlePrepoznavanje skeniranog teksta temeljeno na dubokim modelima
Title (english)Deep Models Based Scanned Text Recognition
AuthorDan Ambrošić
MentorMarko Čupić (mentor)
Committee memberMarko Čupić (predsjednik povjerenstva)
Committee memberMarin Golub (član povjerenstva)
Committee memberLeonardo Jelenković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Iako je optičko prepoznavanje znakova (OCR) uglavnom riješen problem, uvijek ima mjesta za poboljšanja, pogotovo kod obrade povijesnih dokumenata i slika slabije kvalitete. Koristeći pristup CTC, duboki modeli temeljeni na povratnim neuronskim mrežama su uspješno primijenjeni na probleme označavanja sekvenci, kao što je prepoznavanje teksta. Danas često korištena arhitektura neuronskih mreža, koja spaja konvolucijske i povratne slojeve s izlazima CTC i odgovarajućim gubitkom, je isprobana na većem javno dostupnom skupu podataka i na drugom manjem skupu koji je dobiven skeniranjem izabranih knjiga. Drugi skup je označen koristeći aplikaciju s grafičkim korisničkim sučeljem koja je razvijena u sklopu ovog rada i služi za poluautomatsko označavanje teksta na razini znakova. Pokazano je i da unaprijed naučeni model zahtijeva malu količinu novih podataka za učenje kako bi postigao zadovoljavajuće rezultate na drugačijem skupu.
Abstract (english)
Although optical character recognition (OCR) is mostly a solved problem, there is always room for improvement, especially when dealing with historical documents and poor quality images. Using the connectionist temporal classification (CTC) approach, deep models based on recurrent neural networks are successfully applied to sequence labelling tasks, such as text recognition. A commonly used neural network architecture, that combines convolutional and recurrent layers with CTC output and the corresponding loss function, is tested on a bigger publicly available dataset and a smaller one obtained by scanning pages from several books. The second dataset is annotated using a desktop application with a graphical user interface that is developed as a part of this thesis and aids in the character-level text annotation. It is also shown that a pre-trained model requires a small amount of new training data to achieve satisfactory results on a different dataset.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:319432
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
Terms of useLicence In copyright icon
Public note
Created on2020-01-07 15:57:24