  Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional
Networks (DenseNet), have achieved great success for image representation by
discovering deep hierarchical information. However, most existing networks
simply stacks the convolutional layers and hence failing to fully discover
local and global feature information among layers. In this paper, we mainly
explore how to enhance the local and global dense feature flow by exploiting
hierarchical features fully from all the convolution layers. Technically, we
propose an efficient and effective CNN framework, i.e., Fast Dense Residual
Network (FDRN), for text recognition. To construct FDRN, we propose a new fast
residual dense block (f-RDB) to retain the ability of local feature fusion and
local residual learning of original RDB, which can reduce the computing efforts
at the same time. After fully learning local residual dense features, we
utilize the sum operation and several f-RDBs to define a new block termed
global dense block (GDB) by imitating the construction of dense blocks to learn
global dense residual features adaptively in a holistic way. Finally, we use
two convolution layers to construct a down-sampling block to reduce the global
feature size and extract deeper features. Extensive simulations show that FDRN
obtains the enhanced recognition results, compared with other related models.
