  As font is one of the core design concepts, automatic font identification and
similar font suggestion from an image or photo has been on the wish list of
many designers. We study the Visual Font Recognition (VFR) problem, and advance
the state-of-the-art remarkably by developing the DeepFont system. First of
all, we build up the first available large-scale VFR dataset, named AdobeVFR,
consisting of both labeled synthetic data and partially labeled real-world
data. Next, to combat the domain mismatch between available training and
testing data, we introduce a Convolutional Neural Network (CNN) decomposition
approach, using a domain adaptation technique based on a Stacked Convolutional
Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text
images combined with synthetic data preprocessed in a specific way. Moreover,
we study a novel learning-based model compression approach, in order to reduce
the DeepFont model size without sacrificing its performance. The DeepFont
system achieves an accuracy of higher than 80% (top-5) on our collected
dataset, and also produces a good font similarity measure for font selection
and suggestion. We also achieve around 6 times compression of the model without
any visible loss of recognition accuracy.
