  The demands of automated shipping address recognition and verification have
increased to handle a large number of packages and to save costs associated
with misdelivery. A previous study proposed a deep learning system where the
shipping address is recognized and verified based on a camera image capturing
the shipping address and barcode area. Because the system performance depends
on the input image quality, inspection of input image quality is necessary for
image preprocessing. In this paper, we propose an input image quality
verification method combining global and local features. Object detection and
scale-invariant feature transform in different feature spaces are developed to
extract global and local features from several independent convolutional neural
networks. The conditions of shipping label images are classified by fully
connected fusion layers with concatenated global and local features. The
experimental results regarding real captured and generated images show that the
proposed method achieves better performance than other methods. These results
are expected to improve the shipping address recognition and verification
system by applying different image preprocessing steps based on the classified
conditions.
