QR two-dimensional bar code recognition method based on pickup head for chatting

Abstract

The invention discloses a QR two-dimension bar code automatic recognize method based on chat camera, which can quickly and effectively recognize the QR two-dimension bar code read by general chat camera. The invention is not limited in special two-dimension bar code recognizer, to expand the limit condition of input bar code. For the QR bar code image under natural light, the invention designs a self-adaptive threshold division algorism to binary treat the bar code image. When accurately positioning four tops of the QR bar code, based on the character of a position detect image in the QR code, finds three tops, and when positioning the fourth top, using a digging algorism to dig the QR bar code image, to avoid the interference of internal points in Hough conversation, and for resolving the defects of traditional Hough conversation as large calculation and high spatial complexity, the invention designs a Hough conversation crossing known points. Based on the character that the QR code image is square in distortion, the invention uses control point conversation to correct geometry distortion, and uses dual-linear interpolation in gray interpolation in the conversation.

Description

technical field [0001] The invention belongs to the automatic identification technology of electronic barcodes, for the quick response matrix code (or QR two-dimensional barcode, QR two-dimensional barcode, QR Code or QR code for short) generated according to the national standard GB / T 18284-2000 "Quick Response Matrix Code" , based on the popular chat camera for automatic identification. Background technique [0002] The electronic barcode technology commonly used at home and abroad is one-dimensional barcode coding and automatic identification technology. The common coding methods include various standards such as EAN-8, EAN-13, UPC-A and UPC-E. However, the amount of information contained in it is quite small, and it is limited to the use of numbers, which is no longer sufficient in this era of information explosion. Due to the advancement of information technology and hardware technology, we can expand the capacity of electronic barcodes, thus producing the standards an...

AI Technical Summary

Problems solved by technology

Its decoding module uses the K-mean clustering method (K-mean clustering) in the binarization of the barcode image. The disadvantage of this method is that the initial central value of the group is too random, which may enter an infinite loop and fail to achieve expectations. The result; at the same time, the algorithm has a large amount of calculation and consumes a lot of time

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