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The demands of automated shipping address recognition and verification haveincreased to handle a large number of packages and to save costs associatedwith misdelivery. A previous study proposed a deep learning system where theshipping address is recognized and verified based on a camera image capturingthe shipping address and barcode area. Because the system performance dependson the input image quality, inspection of input image quality is necessary forimage preprocessing. In this paper, we propose an input image qualityverification method combining global and local features. Object detection andscale-invariant feature transform in different feature spaces are developed toextract global and local features from several independent convolutional neuralnetworks. The conditions of shipping label images are classified by fullyconnected fusion layers with concatenated global and local features. Theexperimental results regarding real captured and generated images show that theproposed method achieves better performance than other methods. These resultsare expected to improve the shipping address recognition and verificationsystem by applying different image preprocessing steps based on the classifiedconditions.

Fusion of Global-Local Features for Image Quality Inspection of Shipping Label