摘要
针对建筑检测行业中检测报告多、人工归档效率低下的问题,利用模板匹配算法与LeNet框架建立了一套强鲁棒性用于报告文件数字符号的OCR识别系统。针对报告中感兴趣区域(ROI)位置和大小不固定的问题,采用了机器视觉领域中的模板匹配定位算法来定位报告文件的ROI区域。结合LeNet网络与模板匹配定位算法,实现了传统机器视觉方法与人工智能方法的结合,构建了一套检测报告自动归档系统。所构建的自动归档系统的正确归档率达到了95.8%,有效节约了人工成本与时间成本。
In view of the problems of many detection reports and low efficiency of manual filing in the construction inspection industry,a strong robust OCR identification system for digital symbols of report files is established by using template matching algorithm and LeNet framework.Aiming at the problem that the location and size of ROI in the report are not fixed,a template matching location algorithm in the field of machine vision is used to locate the ROI region of the report file.Combined with the matching and positioning algorithm of LeNet network and template,the combination of traditional machine vision method and artificial intelligence method is realized,and a set of automatic filing system of detection report is constructed.The correct filing rate of the automatic archive system is 95.8%,which effectively saves labor cost and time cost.
作者
张辰
陈阳
Zhang Chen;Chen Yang(Guangdong Construction Engineering Quality and Safety Inspection Station Co.,Ltd.,Guangzhou 510500,China;Guangdong Building Research Institute Group Co.,Ltd.,Guangzhou 510500,China)
出处
《信息技术与网络安全》
2021年第8期84-89,共6页
Information Technology and Network Security
关键词
模板匹配
OCR识别
自动归档
template matching
OCR identification
automatic filing
