Version 1
: Received: 3 September 2019 / Approved: 5 September 2019 / Online: 5 September 2019 (12:19:48 CEST)
How to cite:
Büttner, A. Nasḫī – an Efficient Tool for the OCR-Aided Transcription of Printed Texts. Preprints2019, 2019090062. https://doi.org/10.20944/preprints201909.0062.v1
Büttner, A. Nasḫī – an Efficient Tool for the OCR-Aided Transcription of Printed Texts. Preprints 2019, 2019090062. https://doi.org/10.20944/preprints201909.0062.v1
Büttner, A. Nasḫī – an Efficient Tool for the OCR-Aided Transcription of Printed Texts. Preprints2019, 2019090062. https://doi.org/10.20944/preprints201909.0062.v1
APA Style
Büttner, A. (2019). Nasḫī – an Efficient Tool for the OCR-Aided Transcription of Printed Texts. Preprints. https://doi.org/10.20944/preprints201909.0062.v1
Chicago/Turabian Style
Büttner, A. 2019 "Nasḫī – an Efficient Tool for the OCR-Aided Transcription of Printed Texts" Preprints. https://doi.org/10.20944/preprints201909.0062.v1
Abstract
This paper presents a simple yet effective solution for the transcrip- tion of printed texts. Our tool consists of a web-based user interface that provides an easy-to-use and ergonomic workflow and a col- laborative environment for the philologists while allowing them to profit from machine learning OCR technology. As the targeted use case is not mass digitisation but the creation of accurate citable digital editions, the user interface for ground truth production and post correction is built to provide the means for rapid proofread- ing while minimising the amount of errors. The productivity of the setup is further improved by enabling progressive OCR train- ing and recognition in the background to constantly increase the accuracy of the predictions. The advantages of the application are showcased in the second part of the paper by documenting our experiences utilising it for di- gitising Arabic and Latin texts. Over the course of several months the tool has been used to create transcriptions of a wide range of sources, among them challenging early modern editions and Ar- abic scripts, producing a large amount of reusable OCR training data as a positive side effect. Finally, there will be a discussion of possible future extensions of the tool and of how it could be adapted to fit the needs of other digitisation projects.
Keywords
OCR Transcription
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
