Detection and rectification method for bent QR code recognition using convolutional neural networks

Abstract

This paper proposes a method for decoding a bent quick-response code attached to a cylinder. The proposed method consists of two-stage image rectification using the shape function employed in a finite-element-method-based deformation analysis and a pix2pix network, which is a type of generative adversarial network. Rectification based on the shape function requires eight feature points, called nodes, of the bent code. A stacked hourglass network, a convolutional neural network used for human pose estimation, is used to detect these eight nodes. The experimental results show that, compared with other methods, the proposed method can more accurately decode bent codes with larger degrees of curvature.