  Kuzushiji, a cursive writing style, had been used in Japan for over a
thousand years starting from the 8th century. Over 3 millions books on a
diverse array of topics, such as literature, science, mathematics and even
cooking are preserved. However, following a change to the Japanese writing
system in 1900, Kuzushiji has not been included in regular school curricula.
Therefore, most Japanese natives nowadays cannot read books written or printed
just 150 years ago. Museums and libraries have invested a great deal of effort
into creating digital copies of these historical documents as a safeguard
against fires, earthquakes and tsunamis. The result has been datasets with
hundreds of millions of photographs of historical documents which can only be
read by a small number of specially trained experts. Thus there has been a
great deal of interest in using Machine Learning to automatically recognize
these historical texts and transcribe them into modern Japanese characters.
Nevertheless, several challenges in Kuzushiji recognition have made the
performance of existing systems extremely poor. To tackle these challenges, we
propose KuroNet, a new end-to-end model which jointly recognizes an entire page
of text by using a residual U-Net architecture which predicts the location and
identity of all characters given a page of text (without any pre-processing).
This allows the model to handle long range context, large vocabularies, and
non-standardized character layouts. We demonstrate that our system is able to
successfully recognize a large fraction of pre-modern Japanese documents, but
also explore areas where our system is limited and suggest directions for
future work.
