Bi-modal Handwritten Text Recognition ICPR'10 Contest report
Handwritten text is generally captured through two main modalities: off-line and on-line. Each modality has advantages and dis- advantages, but it seems clear that smart approaches to handwritten text recognition (HTR) should make use of both modalities in order to take advantage of the positive aspects of each one. A particularly inter- esting case where the need of this bi-modal processing arises is when an off-line text, written by some writer, is considered along with the on- line modality of the same text written by another writer. This happens, for example, in computer-assisted transcription of old documents, where on-line text can be used to interactively correct errors made by a main off-line HTR system. In order to develop adequate techniques to deal with this challenging bi-modal HTR recognition task, a suitable corpus is needed. We have collected such a corpus using data (word segments) from the publicly available off-line and on-line IAM data sets. In order to provide the Community with an useful corpus to make easy tests, and to establish baseline performance figures, we have proposed this handwritten bi-modal contest. Here is reported the results of the contest with two participants, one of them achieved a 0% classification error rate, whilst the other participant achieved an interesting 1.5%.