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Computer Science > Computer Vision and Pattern Recognition

arXiv:2308.09368 (cs)
[Submitted on 18 Aug 2023]

Title:A tailored Handwritten-Text-Recognition System for Medieval Latin

Authors:Philipp Koch, Gilary Vera Nuñez, Esteban Garces Arias, Christian Heumann, Matthias Schöffel, Alexander Häberlin, Matthias Aßenmacher
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Abstract:The Bavarian Academy of Sciences and Humanities aims to digitize its Medieval Latin Dictionary. This dictionary entails record cards referring to lemmas in medieval Latin, a low-resource language. A crucial step of the digitization process is the Handwritten Text Recognition (HTR) of the handwritten lemmas found on these record cards. In our work, we introduce an end-to-end pipeline, tailored to the medieval Latin dictionary, for locating, extracting, and transcribing the lemmas. We employ two state-of-the-art (SOTA) image segmentation models to prepare the initial data set for the HTR task. Furthermore, we experiment with different transformer-based models and conduct a set of experiments to explore the capabilities of different combinations of vision encoders with a GPT-2 decoder. Additionally, we also apply extensive data augmentation resulting in a highly competitive model. The best-performing setup achieved a Character Error Rate (CER) of 0.015, which is even superior to the commercial Google Cloud Vision model, and shows more stable performance.
Comments: This paper has been accepted at the First Workshop on Ancient Language Processing, co-located with RANLP 2023. This is the author's version of the work. The definite version of record will be published in the proceedings
Subjects: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2308.09368 [cs.CV]
  (or arXiv:2308.09368v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2308.09368

Submission history

From: Matthias Aßenmacher [view email]
[v1] Fri, 18 Aug 2023 08:02:52 UTC (3,249 KB)
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