Agent-based two-dimensional barcode decoding robust against non-uniform geometric distortion

Two-dimensional (2D) codes are assumed to be printed on flat planes and subject to distortion when printed on non-rigid materials such as papers and clothes. Although general 2D code decoders correct uniform distortion such as perspective distortion, it is difficult to correct non-uniform and irregular distortion of 2D code itself. To cope with this problem, this paper proposes an agent-based approach to reconstruct 2D code. In this approach, auxiliary lines are given to a 2D code and used to recognize the distortion. First, the proposed method finds 2D code area using feature patterns composed by the auxiliary lines, and looks for finder patterns by Convolutional Neural Network (CNN). Then, many agents simultaneously trace the lines referring various image features and neighborhood agents. Feature weights are optimized by Genetic Algorithm. Experimental results showed that the proposed method has prospects that it can decode distorted 2D code without occlusion.