Impact of Ground Truth Quality on Handwriting Recognition
Michael Jungo Lars Vögtlin Atefeh Fakhari Nathan Wegmann Rolf Ingold Andreas Fischer Anna Scius-Bertrand
Handwriting recognition is a key technology for accessing the content of old
manuscripts, helping to preserve cultural heritage. Deep learning shows an
impressive performance in solving this task. However, to achieve its full
potential, it requires a large amount of labeled data, which is difficult to
obtain for ancient languages and scripts. Often, a trade-off has to be made
between ground truth quantity and quality, as is the case for the recently
introduced Bullinger database. It contains an impressive amount of over a
hundred thousand labeled text line images of mostly premodern German and Latin
texts that were obtained by automatically aligning existing page-level
transcriptions with text line images. However, the alignment process introduces
systematic errors, such as wrongly hyphenated words. In this paper, we
investigate the impact of such errors on training and evaluation and suggest
means to detect and correct typical alignment errors.