Thesis Details
Adaptace neuronových sítí na cílového pisatele
This bachelor's thesis deals with the adaptation of neural networks to a specific writer with an aim to improve recognition of handwritten text of this specific writer. The method that I use is fast, requires small training dataset and uses regularization, which tries to keep the distribution of regularized weights in adaptation network similar to the one in the original network. I tested this method on dataset of printed text called IMPACT and dataset of handwritten text. When testing on dataset of handwritten text I was able to improve recognition on two diaries with pre adaptation recognition error rate of 10,82 % and 1,82 % to 8,48 % and 0,77 % with a small number of adaptation iterations and using small amount of training lines. When testing on IMPACT dataset I was able to improve recognition error rate from 32,88 % to 5,30 %.
writer adaptation, convolutional neural network, recurrent neural network, text recognition
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT23609,
author = "Jakub Sekula",
type = "Bachelor's thesis",
title = "Adaptace neuronov\'{y}ch s\'{i}t\'{i} na c\'{i}lov\'{e}ho pisatele",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2021,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/23609/"
}