Combining handwriting and speech recognition for transcribing historical handwritten documents

Transcription of historical documents is an interesting task for libraries in order to make available their funds. In the lasts years, the use of Handwritten Text Recognition allowed paleographs to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is obtaining the draft transcription by dictating the contents to an Automatic Speech Recognition system. When both sources (image and speech) are available, a multimodal combination is possible, and an iterative process can be used in order to refine the final hypothesis. In this work, a multimodal combination based on confusion networks is presented. Results on two different sets of data, with different difficulty level, show that the proposed technique provides similar or better draft transcriptions than a previously proposed approach, allowing for a faster transcription process.