Recognition of damaged letters based on neural network analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F13%3AA14017Z6" target="_blank" >RIV/61988987:17310/13:A14017Z6 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Recognition of damaged letters based on neural network analysis

Popis výsledku v původním jazyce

This paper describes an experimental study based on the application of neural networks for pattern recognition of numbers stamped (imprinted) on ingots. The same task was also solved using fuzzy logic. The ability of all tested neural networks is sufficient to learn all the test patterns, as was demonstrated during experimental works. Unfortunately, amount of training patterns provided by Company KMC Group, s.r.o. were very small and they were very different from test samples. In the article, appropriate types of binarization were discussed so as to extract sufficient information regarding classification via neural networks. There were the optimization of the training set proposed based on the training set analysis. Next, we also proposed way of optimization of parameters belonging to adaptation rules of used neural networks. All experimental results were mutually compared in conclusion.

Název v anglickém jazyce

Recognition of damaged letters based on neural network analysis

Popis výsledku anglicky

This paper describes an experimental study based on the application of neural networks for pattern recognition of numbers stamped (imprinted) on ingots. The same task was also solved using fuzzy logic. The ability of all tested neural networks is sufficient to learn all the test patterns, as was demonstrated during experimental works. Unfortunately, amount of training patterns provided by Company KMC Group, s.r.o. were very small and they were very different from test samples. In the article, appropriate types of binarization were discussed so as to extract sufficient information regarding classification via neural networks. There were the optimization of the training set proposed based on the training set analysis. Next, we also proposed way of optimization of parameters belonging to adaptation rules of used neural networks. All experimental results were mutually compared in conclusion.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Mendel 2013

ISBN

978-80-214-4755-4

ISSN

1803-3814

e-ISSN

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Počet stran výsledku

6

Strana od-do

209-214

Název nakladatele

Brno Univerzity of Technology

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

26. 6. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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