Podrobný výpis o publikaci

BANKOVIČ, Mikuláš, Vít NOVOTNÝ a Petr SOJKA. Application of Super-Resolution Models in Optical Character Recognition of Czech Medieval Texts. In Horák, Rychlý, Rambousek. Recent Advances in Slavonic Natural Language Processing (RASLAN 2021). Brno: Tribun EU, 2021, s. 11-18. ISBN 978-80-263-1670-1.

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Základní údaje
Originální název	Application of Super-Resolution Models in Optical Character Recognition of Czech Medieval Texts
Autoři	BANKOVIČ, Mikuláš (703 Slovensko, garant, domácí), Vít NOVOTNÝ (203 Česká republika, domácí) a Petr SOJKA (203 Česká republika, domácí).
Vydání	Brno, Recent Advances in Slavonic Natural Language Processing (RASLAN 2021), od s. 11-18, 8 s. 2021.
Nakladatel	Tribun EU

Další údaje
Originální jazyk	angličtina
Typ výsledku	Stať ve sborníku
Obor	10200 1.2 Computer and information sciences
Stát vydavatele	Česká republika
Utajení	není předmětem státního či obchodního tajemství
Forma vydání	tištěná verze "print"
WWW	Domovská stránka workshopu Full text PDF
Kód RIV	RIV/00216224:14330/21:00119900
Organizační jednotka	Fakulta informatiky
ISBN	978-80-263-1670-1
ISSN	2336-4289
Klíčová slova anglicky	Super-resolution; Optical character recognition; Medieval texts
Změnil	Změnil: RNDr. Pavel Šmerk, Ph.D., učo 3880. Změněno: 15. 5. 2024 10:24.

Anotace
Optical character recognition (OCR) of scanned images is used in multiple applications in numerous domains and several frameworks and OCR algorithms are publicly available. However, some domains such as medieval texts suffer from low accuracy, mainly due to low resources and poor quality data. For such domains, preprocessing techniques help to increase the accuracy of OCR algorithms. In this paper, we experiment with two super-resolution models: Waifu2x and SRGAN. We use the models to reduce noise and increase the image resolution of scanned medieval texts. We evaluate the models on the AHISTO project dataset and compare them against several baselines. We show that our models produce improvements in OCR accuracy.

Anotace

Optical character recognition (OCR) of scanned images is used in multiple applications in numerous domains and several frameworks and OCR algorithms are publicly available. However, some domains such as medieval texts suffer from low accuracy, mainly due to low resources and poor quality data. For such domains, preprocessing techniques help to increase the accuracy of OCR algorithms. In this paper, we experiment with two super-resolution models: Waifu2x and SRGAN. We use the models to reduce noise and increase the image resolution of scanned medieval texts. We evaluate the models on the AHISTO project dataset and compare them against several baselines. We show that our models produce improvements in OCR accuracy.