Glyph Miner
Benedikt Budig, Thomas C. van Dijk, Felix Kirchner · 2016
While off-the-shelf OCR systems work well on many modern documents, the heterogeneity of early prints provides a significant challenge. To achieve good recognition quality, existing software must be "trained" specifically to each particular corpus. This is a tedious process that involves significant user effort. In this paper we demonstrate a system that generically replaces a common part of the training pipeline with a more efficient workflow: Given a set of scanned pages of a historical document, our system uses an efficient user interaction to semi-automatically extract large numbers of occurrences of glyphs indicated by the user. In a preliminary case study, we evaluate the effectiveness of our approach by embedding our system into the workflow at the University Library Würzburg.