@inproceedings{pub5229,
    abstract = {The automatic transcription of historical documents is vital for the creation of digital libraries. In order to make images of valuable old documents amenable to browsing, a transcription of high accuracy is needed. In this paper, two state-of-the art recognizers originally developed for modern scripts are applied to medieval documents. The first is based on Hidden Markov Models and the second uses a Neural Network with a bidirectional Long Short-Term Memory. On a dataset of word images extracted from a medieval manuscript of the 13th century, written in Middle High German by several writers, it is demonstrated that a word accuracy of 93.32% is achievable. This is far above the word accuracy of 77.12% achieved with the same recognizers for unconstrained modern scripts written in English. These results encourage the development of real world systems for automatic transcription of historical documents with a view to image and text browsing in digital libraries.},
    year = {2009},
    title = {Automatic Transcription of Handwritten Medieval Documents},
    booktitle = {Proceedings of the 15th International Conference on Virtual Systems and MultiMedia. International Conference on Virtual Systems and MultiMedia (VSMM-2009), September 9-12, Vienna, Austria},
    pages = {137-142},
    isbn = {978-0-7695-3790-0},
    publisher = {IEEE Computer Society},
    author = {Andreas Fischer and Markus Wuthrich and Marcus Liwicki and Volkmar Frinken and Horst Bunke and Gabriel Viehhauser and Michael Stolz},
    keywords = {Computer Vision for Cultural Heritage},
    url = {http://doi.ieeecomputersociety.org/10.1109/VSMM.2009.26}
}