  Convolutional Neural Networks have made their mark in various fields of
computer vision in recent years. They have achieved state-of-the-art
performance in the field of document analysis as well. However, CNNs require a
large amount of annotated training data and, hence, great manual effort. In our
approach, we introduce a method to drastically reduce the manual annotation
effort while retaining the high performance of a CNN for word spotting in
handwritten documents. The model is learned with weak supervision using a
combination of synthetically generated training data and a small subset of the
training partition of the handwritten data set. We show that the network
achieves results highly competitive to the state-of-the-art in word spotting
with shorter training times and a fraction of the annotation effort.
