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Handwriting recognition is a key technology for accessing the content of oldmanuscripts, helping to preserve cultural heritage. Deep learning shows animpressive performance in solving this task. However, to achieve its fullpotential, it requires a large amount of labeled data, which is difficult toobtain for ancient languages and scripts. Often, a trade-off has to be madebetween ground truth quantity and quality, as is the case for the recentlyintroduced Bullinger database. It contains an impressive amount of over ahundred thousand labeled text line images of mostly premodern German and Latintexts that were obtained by automatically aligning existing page-leveltranscriptions with text line images. However, the alignment process introducessystematic errors, such as wrongly hyphenated words. In this paper, weinvestigate the impact of such errors on training and evaluation and suggestmeans to detect and correct typical alignment errors.

Impact of Ground Truth Quality on Handwriting Recognition