Thesis Details
Zlepšování kvality digitalizovaných textových dokumentů
The aim of this work is to increase the accuracy of the transcription of text documents. This work is mainly focused on texts printed on degraded materials such as newspapers or old books. To solve this problem, the current method and problems associated with text recognition are analyzed. Based on the acquired knowledge, the implemented method based on GAN network architecture is chosen. Experiments are a performer on these networks in order to find their appropriate size and their learning parameters. Subsequently, testing is performed to compare different learning methods and compare their results. Both training and testing is a performer on an artificial data set. Using implemented trained networks increases the transcription accuracy from 65.61 % for the raw damaged text lines to 93.23 % for lines processed by this network.
Neural networks, deep neural networks, convolution neural networks, GAN networks, TensorFlow, image quality enhancement
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Hrubý Martin, Ing., Ph.D. (DITS FIT BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT23086,
author = "Jan Tr\v{c}ka",
type = "Master's thesis",
title = "Zlep\v{s}ov\'{a}n\'{i} kvality digitalizovan\'{y}ch textov\'{y}ch dokument\r{u}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2020,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/23086/"
}