数字字符识别广泛的应用到汽车牌照识别,大规模数据统计,财务、税务等金融领域和邮件分拣等领域中。随着国家信息化进程的加速,手写数字识别的应用需求将越来越广泛,因此应当加强这方面的研究工作。数字字符识别的方法有很多种,比如基于基于最小错误率Bayes决策和最小风险的Bayes决策,基于独立分量分析,人工神经网络等方法。
本论文设计是基于最小错误率Bayes决策和最小风险的Bayes决策的手写数字字符分类。在Visual C++ 6.0的环境下,利用MFC开发出模拟手写环境,通过对手写数字字符的位置定位及其特征的提取,并利用基于最小错误率Bayes决策或最小风险的Bayes决策相关的理论知识,计算出相应判别函数和损失函数的值,并实现对0到9模拟手写字符的分类。
关键字:Visual C++ 6.0,Bayes决策,数字字符识别。
Abstract
Figure recognition is widely used in license screening, large scale data analysis, financial and tax fields and mail sorting. With the acceleration of information development, handwriting figure recognition is in great need, and related research should be stressed. There are many methods of figure recognition, such as Bayes decision of minimal false rate and Bayes decision of least risk, based on individual part analysis, and artificial intellectual network.
The design included in this essay is based on figure recognition of bayes decision of minimal false rate and that of least risk. In the environment of Visual C++ 6.0, we use MFC to develop mimic handwriting situation to get the result of discrimination function and loss function and to realize the categorization of figures 0 to 9 through locating handwriting figures and the traction of related characteristics as well as bayes decision of minimal false rate and least risk.
Keywords:Visual C++ 6.0, Bayes decision, Figure recognition