Research Of Stained Two-Dimensional Code Image Restoration

With the development of computer technology, barcode technology has been welldeveloped. It has all the functions, such as data collection, automatic identification, data correction and so on. Barcode technology has been widely applied in various fields, changing the way of people’s life. In addition, barcode technology has greatly improved the efficiency of labor. At present, the two-dimensional code has been applied to various industries. In a certain sense, the wide application of two-dimensional code technology has greatly changed the mode of production. While in the practical application, the surface of two-dimensional code is often damaged due to perforation and defacement, especially in the circulation of aquatic products, it will make the identification abortive. The purpose of this study is to realize the recognition of stained QR code. QR code itself has certain error correction ability, but when the stained area exceeds the scope of error correction of the code, which will result in the failure of identification. In addition, when the key area is missing or defaced, even if the stained area does not exceed the scope of error correction of the code, it also cannot be identified. In order to solve the problem of stained QR identification, this paper mainly studies the following two aspects:1) This paper proposes using associative memory function of Hopfield neural network to realize the stained two-dimensional code image restoration. Through the detailed analysis of QR code, the associative memory network is designed. In light of the limitations of the traditional Hopfield neural network, an improved Hopfield neural network is presented based on the knowledge of Hopfield neural network. Through modifying the connection relations matrix in learning phase, the function of the network is improved. It designs a discrete Hopfield neural network with associative memory function using MATLAB as the tool, implementing the recovery of different degree of stained two-dimensional code. Experiments using test samples of different pollution rates to verify the improved neural network is more effective. After improved, the recognition rate of stained QR code has greatly improved.2) Using template matching to realize the identification of two-dimensional code. For each QR code, the structure is fixed, but the specifications are different. When the size of the QR code is determined, the number of calibration pattern is also set. In order to reduce the amount of computation and improve the efficiency, we extract the data area as the effective region. Based on the improved similarity calculation formula, the similarity of the effective area is calculated, realizing the match of stained QR code.