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Multimodal Output Combination for Transcribing Historical Handwritten Documents

  • Emilio GranellEmail author
  • Carlos-D. Martínez-Hinarejos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)

Abstract

Transcription of digitalised historical documents is an interesting task in the document analysis area. This transcription can be achieved by using Handwritten Text Recognition (HTR) on digitalised pages or by using Automatic Speech Recognition (ASR) on the dictation of contents. Moreover, another option is using both systems in a multimodal combination to obtain a draft transcription, given that combining the outputs of different recognition systems will generally improve the recognition accuracy. In this work, we present a new combination method based on Confusion Network. We check its effectiveness for transcribing a Spanish historical book. Results on both unimodal combination with different optical (for HTR) and acoustic (for ASR) models, and multimodal combination, show a relative reduction of Word and Character Error Rate of \(14.3\%\) and \(16.6\%\), respectively, over the HTR baseline.

Keywords

Document analysis and transcription Handwritten text recognition Automatic speech recognition Confusion Networks combination Recognition outputs combination 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Pattern Recognition and Human Language Technology Research CenterUniversitat Politècnica de ValènciaValenciaSpain

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