2D bar code is a useful tool which can store a large amount of information, with strong fault tolerant ability. It is easy to crate and transform these codes, so 2D bar codes are widely used in logistics, marketing and entertainment industry. In present, the decoding technology is transiting from traditional photo-electric scanning method to image-based method. Image-based method reads 2D bar code through camera and abstracts bit stream from the image, then, decodes the data to get the information hidden in the 2D bar code. More and more people are concerning research of this method because of its characteristic of low cost, fast and easy operation. However, in practice, the application of image-based decoding technology is confined for complex background, variance codes modality and other factors of image processing. Concrete work and major research results in this paper are as followed: (1) Carried out a detailed analysis on the development of 2D bar codes, industry standard and several common complex background and interference sources. Summarized previous major technical routes and research results of image-based 2D bar code recognition algorithm. In the end, briefly reviewed the content of this paper. (2) Outlined the flow of 2D bar code recognition algorithm offered in this paper. And then focused on segmentation and location of 2D bar codes in images with complex background. This research made full use of the characteristics of bar code image, such as denseness of edges, concentrating of corner points. It also combined segmentation strategy of region growing to implement a region segmentation method based on image processing. Experiments showed that the technical route offered by the paper is adaptive in recognizing different forms of 2D bar codes in complex scenes. (3) Based on the research of this paper, dug deeply into the nature of 2D bar codes and tried to introduce features of texture and machine learning methodology into 2D bar codes recognition framework for improving performance of the system.
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