Application of Super-Resolution Models in Optical Character Recognition of Czech Medieval Texts

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Year of publication 2021
Type Article in Proceedings
Conference Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)
MU Faculty or unit

Faculty of Informatics

Keywords Super-resolution; Optical character recognition; Medieval texts
Description 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.
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