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OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings

Artificial Intelligence and Applied Computer Science, University of Würzburg, 97074 Würzburg, Germany
Center for Information and Language Processing, LMU Munich, 80538 Munich, Germany
Institute for Modern Art History, University of Zurich, 8006 Zurich, Switzerland
Institute for Philosophy, University of Würzburg, 97074 Würzburg, Germany
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(22), 4853;
Received: 6 September 2019 / Revised: 25 October 2019 / Accepted: 1 November 2019 / Published: 13 November 2019
(This article belongs to the Section Computing and Artificial Intelligence)
Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years, great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, character recognition, and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper, we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required ground truth for training stronger mixed models (for segmentation, as well as text recognition) is not available, yet, neither in the desired quantity nor quality. To deal with this issue in the short run, OCR4all offers a comfortable GUI that allows error corrections not only in the final output, but already in early stages to minimize error propagations. In the long run, this constant manual correction produces large quantities of valuable, high quality training material, which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. During experiments, the fully automated application on 19th Century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. Furthermore, on very complex early printed books, even users with minimal or no experience were able to capture the text with manageable effort and great quality, achieving excellent Character Error Rates (CERs) below 0.5%. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings. View Full-Text
Keywords: optical character recognition; document analysis; historical printings optical character recognition; document analysis; historical printings
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MDPI and ACS Style

Reul, C.; Christ, D.; Hartelt, A.; Balbach, N.; Wehner, M.; Springmann, U.; Wick, C.; Grundig, C.; Büttner, A.; Puppe, F. OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings. Appl. Sci. 2019, 9, 4853.

AMA Style

Reul C, Christ D, Hartelt A, Balbach N, Wehner M, Springmann U, Wick C, Grundig C, Büttner A, Puppe F. OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings. Applied Sciences. 2019; 9(22):4853.

Chicago/Turabian Style

Reul, Christian, Dennis Christ, Alexander Hartelt, Nico Balbach, Maximilian Wehner, Uwe Springmann, Christoph Wick, Christine Grundig, Andreas Büttner, and Frank Puppe. 2019. "OCR4all—An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings" Applied Sciences 9, no. 22: 4853.

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