  The subtleties of human perception, as measured by vision scientists through
the use of psychophysics, are important clues to the internal workings of
visual recognition. For instance, measured reaction time can indicate whether a
visual stimulus is easy for a subject to recognize, or whether it is hard. In
this paper, we consider how to incorporate psychophysical measurements of
visual perception into the loss function of a deep neural network being trained
for a recognition task, under the assumption that such information can enforce
consistency with human behavior. As a case study to assess the viability of
this approach, we look at the problem of handwritten document transcription.
While good progress has been made towards automatically transcribing modern
handwriting, significant challenges remain in transcribing historical
documents. Here we describe a general enhancement strategy, underpinned by the
new loss formulation, which can be applied to the training regime of any deep
learning-based document transcription system. Through experimentation, reliable
performance improvement is demonstrated for the standard IAM and RIMES datasets
for three different network architectures. Further, we go on to show
feasibility for our approach on a new dataset of digitized Latin manuscripts,
originally produced by scribes in the Cloister of St. Gall in the the 9th
century.
