1 research outputs found
Expolring Architectures for CNN-Based Word Spotting
The goal in word spotting is to retrieve parts of document images which are
relevant with respect to a certain user-defined query. The recent past has seen
attribute-based Convolutional Neural Networks take over this field of research.
As is common for other fields of computer vision, the CNNs used for this task
are already considerably deep. The question that arises, however, is: How
complex does a CNN have to be for word spotting? Are increasingly deeper models
giving increasingly bet- ter results or does performance behave asymptotically
for these architectures? On the other hand, can similar results be obtained
with a much smaller CNN? The goal of this paper is to give an answer to these
questions. Therefore, the recently successful TPP- PHOCNet will be compared to
a Residual Network, a Densely Connected Convolutional Network and a LeNet
architecture empirically. As will be seen in the evaluation, a complex model
can be beneficial for word spotting on harder tasks such as the IAM Offline
Database but gives no advantage for easier benchmarks such as the George
Washington Database