Generiranje realističnih podataka za treniranje modela dubokog učenja

master's thesis

• Title (english): Generating Realistic Data for Training of Deep Learning Models
• Author: Paolo Čerić
• 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

U ovome radu ispitali smo mogućnosti treniranja Faster R-CNN duboke neuronske mreže nad generiranim podacima kroz slučaj detekcije i klasifikacije znakova na ispisanim računima. Fokus rada bio je na analizi stvarnih podataka te implementaciji sustava za njihovo automatsko generiranje. Analizom rezultata ustanovljena je visoka sličnost generiranog i stvarnog skupa podataka po pitanju mogućnosti lokalizacije znakova, dok je kod klasifikacije uočen znatniji data mismatch odnosno veća razlika generiranog i stvarnog skupa podataka. Predloženo objašnjenje tog problema jest manjak određenih fontova u generiranom skupu podataka, odnosno neki su fontovi prisutni samo u stvarnom skupu, no ne i u generiranom.

Abstract (english)

In this thesis we explored the possibilities of training Faster R-CNN deep neural network on automatically generated dataset, investigating the case of detection and classification of characters on receipts. The focus of the thesis was to analyze real data and to implement a system for its automatic generation. In case of network’s localization capabilities, high similarity between generated and real dataset were detected, meaning low data mismatch was achieved. When it comes to classification, the data mismatch is significantly higher. A proposed explanation of that problem is the lack of certain fonts in the generated dataset, meaning there are some fonts present in the real dataset, but not in the generated one since they are not easily obtainable.

• Keywords: generiranje podataka; duboko učenje; Faster R-CNN; data mismatch; OCR
• Keywords (english): generated dataset; deep learning; Faster R-CNN; data mismatch; OCR
• Language: croatian
• URN:NBN: urn:nbn:hr:168:419756
• 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 19:43:33