Bleed-Through Removal by Learning a Discriminative Color Channel

This paper proposes a novel bleed-through removal technique based on learning a color channel that is optimized so that the foreground text is enhanced while at the same time the variability of the background (including the bleed-through) is diminished. The technique is intended to be part of an interactive transcription system in which the objective is obtaining high quality transcriptions with the least human effort. Thus, instead of training the bleed-through removal to work in general for any document, the technique requires a user to label regions both as foreground text and as bleed-through, with the aim that the method is adapted to the characteristics of each document. The proposal is assessed using the handwritten recognition performance on a real 17th century manuscript.