A Multimodal Approach to Dictation of Handwritten Historical Documents
Handwritten Text Recognition is a problem that has gained at- tention in the last years due to the interest in the transcription of historical documents. Handwritten Text Recognition employs models that are similar to those employed in Automatic Speech Recognition (Hidden Markov Models and n-grams). Dictation of the contents of the document is an alternative to text recogni- tion. In this work, we explore the performance of a Handwritten Text Recognition system against that of two speech dictation systems: a non-multimodal system that only uses speech and a multimodal system that performs a text recognition which is used in the posterior speech recognition. Results show that the multimodal combination outperforms any of the other consid- ered non-multimodal systems.