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master's thesis
Unaprjeđenje algoritma za prepoznavanje tekstualnih znakova
Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017. urn:nbn:hr:200:802903
Brisinello, Matteo
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Computer Engineering and Automation
Chair of Automation and Robotics

Institutional repository: Faculty of Electrical Engineering, Computer Science and Information Technology Osijek

Cite this document

Brisinello, M. (2017). Unaprjeđenje algoritma za prepoznavanje tekstualnih znakova (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:802903

Brisinello, Matteo. "Unaprjeđenje algoritma za prepoznavanje tekstualnih znakova." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017. https://urn.nsk.hr/urn:nbn:hr:200:802903

Brisinello, Matteo. "Unaprjeđenje algoritma za prepoznavanje tekstualnih znakova." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2017. https://urn.nsk.hr/urn:nbn:hr:200:802903

Brisinello, M. (2017). 'Unaprjeđenje algoritma za prepoznavanje tekstualnih znakova', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 20 May 2024, https://urn.nsk.hr/urn:nbn:hr:200:802903

Brisinello M. Unaprjeđenje algoritma za prepoznavanje tekstualnih znakova [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2017 [cited 2024 May 20] Available at: https://urn.nsk.hr/urn:nbn:hr:200:802903

M. Brisinello, "Unaprjeđenje algoritma za prepoznavanje tekstualnih znakova", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:200:802903

Metadata
TitleUnaprjeđenje algoritma za prepoznavanje tekstualnih znakova
Title (english)Optical character recognition algorithm improvement
AuthorMatteo Brisinello
MentorRatko Grbić (mentor)
Committee memberRatko Grbić (predsjednik povjerenstva)
Committee memberMario Vranješ (član povjerenstva)
Committee memberMarijan Herceg (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Computer Engineering and Automation)
(Chair of Automation and Robotics)
Osijek
Defense date and country2017-10-03, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Information Systems
Abstract
Točnost algoritma za prepoznavanje tekstualnih znakova (engl. Optical character recognition - OCR) na slikama dohvaćenih sa set-top box (STB) uređaja igra vrlo važnu ulogu pri testiranju istih. Međutim, pokretanje algoritma za prepoznavanje tekstualnih znakova na takvim slikama često rezultira niskim postotkom točnosti. Uzrok tomu su navedene slike koje mogu biti niske rezolucije, loše kvalitete ili mogu sadržavat šarenu pozadinu. U ovom radu preporučene su četiri metode predobrade slike kako bi se poboljšala točnost prepoznavanja tekstualnih znakova. Ispitane su točnosti na slikama dohvaćenih sa STB uređaja korištenjem programa Tesseract 3.5 te relativno nove verzije Tesseract 4.0. Na tim slikama točnost prepoznavanja pomoću Tesseract-a 3.5 je 35.7%, dok Tesseract 4.0 ostvaruje točnost od 70.2%. Metode ispitane u ovom radu unaprjeđuju točnost algoritma za prepoznavanje tekstualnih znakova za 33.3% za Tesseract 3.5 te za 22.6% za Tesseract 4.0. Također je ispitana metoda koja odvaja tekst od pozadine kada su u pitanju slike sa šarenom pozadinom. Točnost prepoznavanja tekstualnih znakova nakon izvršavanja navedene metode je 94.4%. Također, izgrađen je dodatni korak automatizacije procesa prepoznavanja tekstualnih znakova na slikama sa šarenom pozadinom pristupom strojnog učenja. Navedeni pristup detektira sliku na kojoj je prikazan tekst nakon odvajanja teksta od pozadine. Testiranje istog rezultiralo je s točnošću od 95,5%.
Abstract (english)
Efficient Optical Character Recognition (OCR) in images grabbed from Set-Top Boxes (STBs) plays an important role in STB testing. However, running OCR software on such images usually ends with low OCR performance since images can have low resolution, low image quality or colorful background. In order to improve OCR performance, four different image preprocessing methods are proposed. In this paper OCR is performed with Tesseract 3.5 and the relatively new Tesseract 4.0 on the images grabbed from different STBs. On the original images Tesseract 3.5 provides a 35.7% accuracy while Tesseract 4.0 attains a 70.2% accuracy. The proposed preprocessing methods improve OCR performance by 33.3% for Tesseract 3.5 and 22.6% for Tesseract 4.0 on the available images. On images with colorful background, a method which separates text from background is proposed. OCR performance after separating text from background results with an accuracy of 94,4%. Aditionally, a step of automation in a process of reading text from images with colorful background was build with an approach of machine learning. This approach detects the image containing text after the separation from the background with the accuracy of 95,5%.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:802903
Study programmeTitle: Graduate University Study Programme in Computer Engineering
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 conditionsOpen access
Terms of useLicence In copyright icon
Created on2017-10-27 12:07:15