  The Houma Alliance Book is one of the national treasures of the Museum in
Shanxi Museum Town in China. It has great historical significance in
researching ancient history. To date, the research on the Houma Alliance Book
has been staying in the identification of paper documents, which is inefficient
to identify and difficult to display, study and publicize. Therefore, the
digitization of the recognized ancient characters of Houma League can
effectively improve the efficiency of recognizing ancient characters and
provide more reliable technical support and text data. This paper proposes a
new database of Houma Alliance Book ancient handwritten characters and a
multi-modal fusion method to recognize ancient handwritten characters. In the
database, 297 classes and 3,547 samples of Houma Alliance ancient handwritten
characters are collected from the original book collection and by human
imitative writing. Furthermore, the decision-level classifier fusion strategy
is applied to fuse three well-known deep neural network architectures for
ancient handwritten character recognition. Experiments are performed on our new
database. The experimental results first provide the baseline result of the new
database to the research community and then demonstrate the efficiency of our
proposed method.
