Research On Recognition Algorithm Of QR Code And The Implementation In Embedded System

Barcode technology is the newly recognition technology that integrated with the technology of computer, communication and auto-recognition. Based on information coding principle, the barcode recognition decodes the code through specific barcode reader. After the development of decades of years, this technology has played an increasingly board role in much domain of the society. The kinds of barcode can be divided into one-dimension code and two-dimension code currently. The one-dimension code has a wide range of application due to its simple construction and recognition, but mainly has the defects of small information capacity and strong dependency with the database. The two-dimension code has made up one-dimension code's disadvantages mostly. With the big capacity of information, the two-dimension code can be independent data file to make the data transmission more efficiently. Among kinds of two-dimension code, with the special Chinese character coding principle, the QR code has a brilliant application prospect in areas using Chinese characters. The research of QR code currently focuses on making the recognition algorithm more quickly and effectively.The subject this thesis researched is based on the QR code, proposes a solution to recognize the code with S3C2440A microprocessor and CMOS image sensor integrated with the technology of embedded and digital image process. The usage ofμC/OS-Ⅱ, an embedded operation system, can ensure all modules of the whole system running stable and effectively.The research focus of this subject is proposing an optimization algorithm to achieve the target of full recognition of QR code quickly and effectively. Analyzed about the existing algorithm, the proposed algorithm arrived from the symbol character of QR code, optimizes the barcode recognition, positioning and symbol information extraction algorithm. The experience result shows that the propose algorithm has the feature of stable, quickly and effectively recognition under the general image capture environment.