Automatic selection of binarization method from images with serial numbers on industrial products
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F20%3A10247099" target="_blank" >RIV/61989100:27360/20:10247099 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.37904/metal.2020.3636" target="_blank" >http://dx.doi.org/10.37904/metal.2020.3636</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.37904/metal.2020.3636" target="_blank" >10.37904/metal.2020.3636</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Automatic selection of binarization method from images with serial numbers on industrial products
Popis výsledku v původním jazyce
The article deals with the automatic selection of the binarization method using advanced methods of artificial intelligence. The input images to the algorithms are images of serial numbers from industrial environments, for example on iron and steel billets, slabs, etc. The surface of these products is in most cases severely damaged by industrial processes, such as traces of cut, rust, noise, surface roughness, etc. Text recognition is a very common topic nowadays. All investigated solutions are based on the fact that each image is binarized by a single defined method and the accuracy of recognition is given only by the quality of learning of the neural network. Especially in an industrial environment, it is difficult to create a universal method for unambiguous methods for text recognition. The innovation described in this article is the automatic selection of the binarization method (from the Bradley, Niblack, Sauvola methods etc.), which increases the accuracy already in the phase before the text recognition itself, which with the subsequent correct combination of filters leads to an overall increase in accuracy. (C) 2020 TANGER Ltd., Ostrava.
Název v anglickém jazyce
Automatic selection of binarization method from images with serial numbers on industrial products
Popis výsledku anglicky
The article deals with the automatic selection of the binarization method using advanced methods of artificial intelligence. The input images to the algorithms are images of serial numbers from industrial environments, for example on iron and steel billets, slabs, etc. The surface of these products is in most cases severely damaged by industrial processes, such as traces of cut, rust, noise, surface roughness, etc. Text recognition is a very common topic nowadays. All investigated solutions are based on the fact that each image is binarized by a single defined method and the accuracy of recognition is given only by the quality of learning of the neural network. Especially in an industrial environment, it is difficult to create a universal method for unambiguous methods for text recognition. The innovation described in this article is the automatic selection of the binarization method (from the Bradley, Niblack, Sauvola methods etc.), which increases the accuracy already in the phase before the text recognition itself, which with the subsequent correct combination of filters leads to an overall increase in accuracy. (C) 2020 TANGER Ltd., Ostrava.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
METAL 2020 : 29th International Conference on Metallurgy and Materials : conference proceedings : May 20-22, 2020, Brno, Czech Republic, EU
ISBN
978-80-87294-97-0
ISSN
2694-9296
e-ISSN
—
Počet stran výsledku
5
Strana od-do
1357-1361
Název nakladatele
Tanger
Místo vydání
Ostrava
Místo konání akce
Brno
Datum konání akce
20. 5. 2020
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—