摘要
从整幅图像中圈定条码区域和基于运动的多条码识别即是去除运动造成的模糊一直以来是国内的难点,如何快速有效对条码进行定位与识别。本文研究了基于国密SM2加解密的视频流的运动的多条码识别技术。首先探讨了运动图像去模糊技术,一维条码的定位分割技术,即在条码中分割出单行码字符号的图像,本文主要采用模板匹配方法对条码进行定位分割,采用8个方向的模板对条码进行匹配;其次由于运动条码会存在扭曲,需要进行矫正、进行识别,根据条码的识别原理译码,统计各黑白模块宽度,获取条空序列,然后查找码字表获得码字序列,由于一维条码存储信息量少,需要查询数据库进行关联获得条码的产地、日期等可以追溯到的敏感信息,采用国密的SM2对敏感信息加密,最后在追溯查询阶段对敏感信息进行解密识别出条码。实验结果表明,该识别算法具有优秀的性能,显著地提高了条码的识别率,在产品入库阶段满足了实际使用的要求。
The delineation of the bar code from the whole image area and the bar code identification based on multi-movement, i.e. the removal of the blur by the motion, is a difficulty on how to quickly and effectively find the barcode for recognition and location. In this paper, we studied the multi-bar code recognition technology based on the SM2 encryption and decryption of the motion of the video stream. First of all, the paper discusses the motion image deblurring techniques, positioning the one-dimensional bar code segmentation technology—the segmentation of single row codeword in the bar code symbols. This paper mainly uses the method of template matching to locate barcode segmentation and adopts 8-direction template for barcode matching. Secondly there will be distortions of bar code due to the movement, and there is a need to be corrected and be identified, with the decoding according to the principle of identification, the statistics of width of black and white modules, getting empty sequences, and the foundation of the codeword table for the codeword sequence. Because the amount of information stored in the barcode is lacking, we need to query the database to obtain related barcode origin, the date and the information which can be traced back, use the country’s dense SM2 encrypting sensitive information, and identify the bar code at the end of the encryption of sensitive information in the back stage. Experimental results show that the recognition algorithm has excellent performance, significantly improves the recog-nition rate of the bar code, and meets the requirements of the actual use in the product storage stage. 
出处
《计算机科学与应用》
2016年第1期58-64,共7页
Computer Science and Application
基金
科技部科技型中小企业创新基金项目(13C26212201281)
吉林省科技型中小企业创新基金项目(SC201201015)
吉林省科技计划项目(20140519020JH)。
