基于OpenCV视觉库的手写数字识别系统  

Handwritten Digital Recognition System Based on Visual Library OpenCV

作　　者：周原锐 张逸群 曹远航 孙慧慧[1] ZHOU Yuanrui;ZHANG Yiqun;CAO Yuanhang;SUN Huihui(College of Instrumentation&Electrical Engineering,Jilin University,Changchun 130061,China)

机构地区：[1]吉林大学仪器科学与电气工程学院,长春300061

出　　处：《吉林大学学报（信息科学版）》2021年第5期602-608,共7页Journal of Jilin University（Information Science Edition）

基　　金：国家级大学生创新训练基金资助项目(201910183386)。

摘　　要：运行在电脑的手写数字识别系统在移动性和便捷性方面存在诸多缺陷,为此,将数字识别算法移植于灵活小巧的高性能嵌入式设备,设计了一种基于视觉库OpenCV(Open Source Computer Vision Library)的手写数字识别系统。通过舵机云台调整拍摄角度,利用图片拼接和数字分割技术,实现短距离、大面积的手写数字识别。比较了使用KNN(K-Nearest Neighbor)、支持向量机和人工神经网络3种分类算法训练的模型在识别速度、识别准确率、模型体积等方面的区别,经过测试,使用人工神经网络算法在树莓派上的识别时间可低至0.115 s,识别准确率可达72%,具有一定的应用价值。There are many defects in the mobility and convenience of the handwritten digit recognition system running on the computer.In order to make improvement for these defects,a handwritten digit recognition system based on the visual library OpenCV is designed,which transplants the digit recognition algorithm into the flexible and small high-performance embedded equipment.By adjusting the shooting Angle of the steering gear and using the technology of picture splicing and digital segmentation,the handwritten digit recognition of short distance and large area is realized.The recognition speed,recognition accuracy and model volume of the models trained by KNN(K-Nearest Neighbor),support vector machine and artificial neural network are compared.After testing,the identification time of Raspberry Pi by using the artificial neural network algorithm can be as low as 0.115 s,and the recognition accuracy can reach 72%,which has a certain application value.

关 键 词：手写数字识别 人工神经网络 模型训练 OpenCV视觉库 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]