  Recent researches introduced fast, compact and efficient convolutional neural
networks (CNNs) for offline handwritten Chinese character recognition (HCCR).
However, many of them did not address the problem of network interpretability.
We propose a new architecture of a deep CNN with high recognition performance
which is capable of learning deep features for visualization. A special
characteristic of our model is the bottleneck layers which enable us to retain
its expressiveness while reducing the number of multiply-accumulate operations
and the required storage. We introduce a modification of global weighted
average pooling (GWAP) - global weighted output average pooling (GWOAP). This
paper demonstrates how they allow us to calculate class activation maps (CAMs)
in order to indicate the most relevant input character image regions used by
our CNN to identify a certain class. Evaluating on the ICDAR-2013 offline HCCR
competition dataset, we show that our model enables a relative 0.83% error
reduction while having 49% fewer parameters and the same computational cost
compared to the current state-of-the-art single-network method trained only on
handwritten data. Our solution outperforms even recent residual learning
approaches.
