How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR

Phillip Benjamin Ströbel, Simon Clematide, Martin Volk


Abstract
Recent advances in Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) have led to more accurate textrecognition of historical documents. The Digital Humanities heavily profit from these developments, but they still struggle whenchoosing from the plethora of OCR systems available on the one hand and when defining workflows for their projects on the other hand.In this work, we present our approach to build a ground truth for a historical German-language newspaper published in black letter. Wealso report how we used it to systematically evaluate the performance of different OCR engines. Additionally, we used this ground truthto make an informed estimate as to how much data is necessary to achieve high-quality OCR results. The outcomes of our experimentsshow that HTR architectures can successfully recognise black letter text and that a ground truth size of 50 newspaper pages suffices toachieve good OCR accuracy. Moreover, our models perform equally well on data they have not seen during training, which means thatadditional manual correction for diverging data is superfluous.
Anthology ID:
2020.lrec-1.436
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3551–3559
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.436
DOI:
Bibkey:
Cite (ACL):
Phillip Benjamin Ströbel, Simon Clematide, and Martin Volk. 2020. How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 3551–3559, Marseille, France. European Language Resources Association.
Cite (Informal):
How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR (Ströbel et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.436.pdf