Višeobjektni modeli detekcije za raspoznavanje teksta dubokim učenjem

master's thesis

• Title (english): Multi-Object Detection Models for Optical Character Recognition Using Deep Learning
• Author: Ivan Jurin
• Mentor: Sven Lončarić (mentor)
• Committee member: Sven Lončarić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2017-07-11, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Raspoznavanje teksta je važna primjena analize slike te se primjenjuje u mnogim područjima ljudske djelatnosti. Istraživanja su pokazala da pristupi temeljeni na dubokom učenju daju visoku točnost u mnogim područjima. U okviru ovog diplomskog rada proučene su primjene metoda dubokog učenja za višeobjektnu detekciju, a jedna od njih, \textit{Faster R-CNN} je iskorištena u problemu raspoznavanja teksta. Predložene su brojne prilagodbe i poboljšanja modela kojima pristup znatno nadmašuje klasične metode u raspoznavanju teksta.

Abstract (english)

Text recognition is an important application of image analysis with applications in many real life situations. Studies have found that approaches based on deep learning achieve excellent results in many areas. Deep learning methods for multiple object detection have been examined within this master's thesis. One of them, \textit{Faster R-CNN} is used for text recognition problem. Many adaptations and improvements of the approach have been submitted. With these modifications the approach excels classsic approaches in text recognition problem.

• Keywords: raspoznavanje teksta; višeobjektna detekcija; duboko učenje; računalni vid
• Keywords (english): text recognition; muli-object detection; deep learning; computer vision
• Language: croatian
• URN:NBN: urn:nbn:hr:168:143570
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-13 21:21:22