Interactive Off-Line Handwritten Text Transcription Using On-Line Handwritten Text as Feedback

Handwritten Text Recognition is a problem that has gained attention in the last years mainly due to the interest in the transcription of historical documents. However, the automatic transcription is ineffectual in unconstrained handwritten documents. Thus, human intervention is typically needed to correct the results. Given that a post-editing approach is inefficient and uncomfortable, multimodal interactive approaches have begun to emerge in the last years. In this scheme, the user interacts with the system by means of an e-pen. This multimodal feedback, on the one hand, allows to improve the accuracy of the system and, on the other hand, increases user acceptability. In this work, we present a new approach on interaction based on character sequences. Here we present developments that allow taking advantage of interaction-derived context to significantly improve feedback decoding accuracy. Empirical tests suggest that, despite the loss of the deterministic accuracy of traditional peripherals, this approach can save significant amounts of user effort with respect to non-interactive post-editing correction.