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

TL;DR

This paper introduces a novel image quality verification method that combines global and local features using multiple CNNs to improve shipping label recognition accuracy.

Contribution

It proposes a fusion approach of global and local features for image quality assessment, enhancing shipping label verification performance.

Findings

Outperforms existing methods in image quality classification

Improves shipping address recognition accuracy

Effective fusion of features from multiple CNNs

Abstract

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.