Abstract
In this paper we propose a pipeline for processing of scanned historical documents into the electronic text form that could then be indexed and stored in a database. The nature of the documents presents a substantial challenge for standard automated techniques – not only there is a mix of typewritten and handwritten documents of varying quality but the scanned pages often contain multiple documents at once. Moreover, the language of the texts alternates mostly between Russian and Ukrainian but other languages also occur. The paper focuses mainly on segmentation, document type classification, and image preprocessing of the scanned documents; the output of those methods is then passed to the off-the-shelf OCR software and a baseline performance is evaluated on a simplified OCR task.
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Acknowledgments
This research was supported by the Ministry of Culture Czech Republic, project No. DG20P02OVV018. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
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Gruber, I. et al. (2020). An Automated Pipeline for Robust Image Processing and Optical Character Recognition of Historical Documents. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2020. Lecture Notes in Computer Science(), vol 12335. Springer, Cham. https://doi.org/10.1007/978-3-030-60276-5_17
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DOI: https://doi.org/10.1007/978-3-030-60276-5_17
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