  Convolutional Recurrent Neural Network (CRNN) is a popular network for
recognizing texts in images. Advances like the variant of CRNN, such as Dense
Convolutional Network with Connectionist Temporal Classification, has reduced
the running time of the network, but exposing the inner computation cost and
weight size of the convolutional networks as a bottleneck. Specifically, the
DenseNet based models utilize the dense blocks as the core module, but the
inner features are combined in the form of concatenation in dense blocks. As
such, the number of channels of combined features delivered as the input of the
layers close to the output and the relevant computational cost grows rapidly
with the dense blocks getting deeper. This will severely bring heavy
computational cost and big weight size, which restrict the depth of dense
blocks. In this paper, we propose a compressed convolution block called
Lightweight Dense Block (LDB). To reduce the computing cost and weight size, we
re-define and re-design the way of combining internal features of the dense
blocks. LDB is a convolutional block similarly as dense block, but it can
reduce the computation cost and weight size to (1/L, 2/L), compared with
original ones, where L is the number of layers in blocks. Moreover, LDB can be
used to replace the original dense block in any DenseNet based models. Based on
the LDBs, we propose a Compressed DenseNet (CDenseNet) for the lightweight
character recognition. Extensive experiments demonstrate that CDenseNet can
effectively reduce the weight size while delivering the promising recognition
results.
