Augmenting Historical Alphabet Datasets Using Generative Adversarial Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F23%3A92542" target="_blank" >RIV/60460709:41110/23:92542 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-21438-7_11" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-21438-7_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-21438-7_11" target="_blank" >10.1007/978-3-031-21438-7_11</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Augmenting Historical Alphabet Datasets Using Generative Adversarial Networks

Popis výsledku v původním jazyce

In this paper, we present a method for expanding small classification datasets. Every research project is based on data and methods, including text analysis. When analyzing historical texts in different alphabets, there are not always Optical Character Recognition algorithms available and, in many cases, such texts need to be transliterated and translated manually, or alternatively, an OCR algorithm can be developed. In order to create such an algorithm, a large volume of input data is needed - each alphabet consists of elementary data - either letters, vowels, or in some cases ideograms. The texts need to be segmented into such elements, and then, the elements are classified. In many cases, it is very difficult and time-costly to get a sufficient amount of data, and it is advisable to use augmentation methods. In our research, we propose using Generative Adversarial Network to expand a relatively small dataset of Palmyrene letters and prove that even by adding generated data equal to the third of size of the original dataset, the classification results are improved by 120 percent.

Název v anglickém jazyce

Augmenting Historical Alphabet Datasets Using Generative Adversarial Networks

Popis výsledku anglicky

In this paper, we present a method for expanding small classification datasets. Every research project is based on data and methods, including text analysis. When analyzing historical texts in different alphabets, there are not always Optical Character Recognition algorithms available and, in many cases, such texts need to be transliterated and translated manually, or alternatively, an OCR algorithm can be developed. In order to create such an algorithm, a large volume of input data is needed - each alphabet consists of elementary data - either letters, vowels, or in some cases ideograms. The texts need to be segmented into such elements, and then, the elements are classified. In many cases, it is very difficult and time-costly to get a sufficient amount of data, and it is advisable to use augmentation methods. In our research, we propose using Generative Adversarial Network to expand a relatively small dataset of Palmyrene letters and prove that even by adding generated data equal to the third of size of the original dataset, the classification results are improved by 120 percent.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Data Science and Algorithms in Systems

ISBN

978-3-031-21438-7

ISSN

2367-3389

e-ISSN

—

Počet stran výsledku

10

Strana od-do

132-141

Název nakladatele

Springer

Místo vydání

Gewerbestrasse 11, 6330 Cham, Switzerland

Místo konání akce

online (Praha)

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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