@book{oai:kansai-u.repo.nii.ac.jp:00022599,
 author = {宮川, 創},
 month = {Mar},
 note = {This paper discusses the differences and suitable uses of three handwritten text recognition (HTR) programs developed in Europe: Transkribus, eScriptorium/Kraken, and OCR4all. It commences with an overview of deep learning, HTR, and OCR (optical character recognition) before progressing to review the three programs of interest from the perspectives of history, developer, accuracy rate, layout recognition (including writing orientation), user experience, and cost. All three programs use deep-learning machine-learning technologies. They have also all been proven to reach accuracy rates of close to one hundred percent when appropriately trained depending on the quality of the images of handwritten text, training data, and validation data. Second, the user experience is very important; Transkribus has the simplest installation procedure and graphical user interface, while OCR4all and eScriptorium require users to have expert computer skills. Third, in terms of cost, users of Transkribus are required to purchase credits to access the system and use HTR models to recognize a new text, while eScriptorium and OCR4all do not rely on credit purchase. Finally, we conclude this paper with an overview of suitable cases for each program.},
 publisher = {関西大学アジア・オープン・リサーチセンター},
 title = {ディープラーニングを用いた歴史的手書き文献の自動翻刻 : コーパス開発の効率化に向けて},
 year = {2022}
}