  Handwritten text recognition for historical documents is an important task
but it remains difficult due to a lack of sufficient training data in
combination with a large variability of writing styles and degradation of
historical documents. While recurrent neural network architectures are commonly
used for handwritten text recognition, they are often computationally expensive
to train and the benefit of recurrence drastically differs by task. For these
reasons, it is important to consider non-recurrent architectures. In the
context of handwritten date recognition, we propose an architecture based on
the EfficientNetV2 class of models that is fast to train, robust to parameter
choices, and accurately transcribes handwritten dates from a number of sources.
For training, we introduce a database containing almost 10 million tokens,
originating from more than 2.2 million handwritten dates which are segmented
from different historical documents. As dates are some of the most common
information on historical documents, and with historical archives containing
millions of such documents, the efficient and automatic transcription of dates
has the potential to lead to significant cost-savings over manual
transcription. We show that training on handwritten text with high variability
in writing styles result in robust models for general handwritten text
recognition and that transfer learning from the DARE system increases
transcription accuracy substantially, allowing one to obtain high accuracy even
when using a relatively small training sample.
