Automatic Transcription of Handwritten Medieval Documents

• Published in: 2009 15th International Conference on Virtual Systems and Multimedia pp. 137 - 142
• Main Authors: Fischer, A., Wuthrich, M., Liwicki, M., Frinken, V., Bunke, H., Viehhauser, G., Stolz, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2009
• Subjects: Artificial intelligence; Computer science; Computer Vision for Cultural Heritage; Handwriting recognition; Hidden Markov models; Mathematics; Multimedia systems; Neural networks; Software libraries; Vocabulary; Writing
• ISBN: 0769537901; 9780769537900
• DOI: 10.1109/VSMM.2009.26

The automatic transcription of historical documents is vital for the creation of digital libraries.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 These results encourage the development of real world systems for automatic transcription of historical documents with a view to image and text browsing in digital libraries.