• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2015, Vol. 37 ›› Issue (12): 2399-2404.

• 论文 • 上一篇    下一篇

基于iPhone手机的数字码实时识别与应用

成一功1,2 ,李国和1,2,3,林仁杰1,2,何云1,2,吴卫江1,2,3,洪云峰3,周晓明3   

  1. (1.中国石油大学(北京)地球物理与信息工程学院,北京 102249;
    2.中国石油大学(北京)油气数据挖掘北京市重点实验室,北京 102249;
    3.石大兆信数字身份管理与物联网技术研究院,北京 100029)
  • 收稿日期:2014-12-20 修回日期:2015-03-12 出版日期:2015-12-25 发布日期:2015-12-28
  • 基金资助:

    国家高新技术研究发展计划资助项目(2009AA062802);国家自然科学基金资助项目(60473125);中国石油(CNPC)石油科技中青年创新基金资助项目(05E7013);国家重大专项子课题资助项目(G580008ZSWX)

Recognition of printednumerical codes
and its application based on iPhones   

CHENG Yigong1,2,LI Guohe1,2,3,LIN Renjie1,2,HE Yun1,2,WU Weijiang1,2,3,HONG Yunfeng3,ZHOU Xiaoming3   

  1. (1.College of Geophysics and Information Engineering,China University of Petroleum,Beijing 102249;
    2.Beijing Key Lab of Data Mining for Petroleum Data,China University of Petroleum,Beijing 102249;
    3.PanPass Institute of Digital Identification Management and Internet of Things,Beijing 100029,China)
  • Received:2014-12-20 Revised:2015-03-12 Online:2015-12-25 Published:2015-12-28

摘要:

根据苹果手机拍摄防伪标签数字实时识别的需要,针对防伪数字字号较小的因素和苹果手机因拍摄距离的原因造成的图像缩小、数字模糊、背景复杂等问题进行处理,提高识别精度。首先通过人工选取数字码区域,并进行背景数字分离,定位获取数字图像;其次采用灰度化和二值化得到黑白图像;然后通过投影对数字码图像进行分割,并对每个数字图像进行归一化、锐化和细化;基于统计学抽取数字码的特征,采用最近邻域判别函数进行数字码识别,取得很好的识别效果。

关键词: 数字码标签, 图像处理, 数字码识别, iPhone手机

Abstract:

To meet the need of realtime shoot and identification of numerical codes printed on goods labels, due to the small size of numerical codes and the small, fuzzy, complex images taken by iPhones from long distance, we propose a series of image processing to improve the recognition accuracy. First, we choose numerical regions manually, and then separate numerical codes from the background, thus obtaining a numerical image. Then the numerical image is transformed to a whiteblack one by graying and binaryzation. Each numerical code image is segmented by projection method, and then they are normalized, sharpened, and thinned. We finally adopt the nearest neighbor method to recognize the numerical codes based on the extracted statistical features. Experimental results prove the high recognition accuracy.

Key words: numerical code label;image processing;numeral recognition;iPhone