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

Handwritten Text Recognition has gained attention in the last years mainly due to the interest in the transcription of historical documents. However, automatic transcription is ineffectual in unconstrained handwritten documents, thus human intervention is typically needed to correct the results, even though post-editing is generally inefficient and uncomfortable. To alleviate these problems, 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 not only allows us to improve the accuracy of the system but also increases user acceptability. In this work, we present a new approach for interaction based on character sequences. 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.