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.

拜仁学院的中世纪拉丁字典数字化工作，采用了端到端的流水线，包括定位、提取和转录手写词条等步骤。使用最新的图像分割模型准备初始数据集，并尝试不同的基于 Transformer 的模型进行实验，采用丰富的数据增强技术，最佳设置的字符错误率为 0.015，表现优于商业的谷歌云视觉模型并且更加稳定。