High density bar codes are very popular today due to large storage capacity and small code area. But unfortunately, high density versions of QR codes are not being used due to reading problems with most smartphones and flatbed scanner QR code applications. Due to frequent changes and small sizes of the black and white modules, there are reading problems in the QR code binarization and tilt correction processes. The binarization method sets the global or local threshold, and binarizes each pixel separately, that is why they are sensitive to print-and-scan distortion and luminosity. In this paper, we focus on the recognition of high density QR codes. We propose to use the centrality bias of each module to improve the module recognition results. This measure has been used for proposed classification methods and for standard QR code recognition methods. In both cases, the recognition rate was improved, as confirmed by the experimental results.