摘 要
目前，模式識別領域在日常生活中的應用已經越來越廣泛，比如人臉、指紋識別，字符識別，車牌識別。所以，對字符識別進行學習與研究是非常有必要的。
本課題為數字字符識別模擬演示系統。主要是利用正態分布下的最小錯誤率Bayes方法和最小風險Bayes方法，來實現手寫數字從0到9的識別。該系統首先是實現模擬手寫數字；然后利用5*5的模板提取出樣品的特征，采用模板可以使同一形狀、不同大小的樣品得到歸一化的特征提取，所以有能力對同一形狀、不同大小的樣品視為同類；最后結合Bayes決策進行判別。使用最小錯誤率Bayes方法，在判別過程中能使錯誤率達到最小，即使錯分類出現的可能性最小，而最小風險Bayes方法，在判別過程中可以使風險達到最小，減少危害大的錯分類情況。
本設計是利用Visual C++ 6.0實現的，實驗證明，該系統對于模擬手寫的數字基本上能正確識別，但是對于手寫不規范的數字會存在錯判的情況，這跟樣品庫的有限有關。
關鍵詞： 最小錯誤,最小風險,特征選擇,模擬手寫,MFC
ABSTRACT
Now,the application of Pattern Recognition is more and more popular in our daily life,for example the man’s Face、Fingerprint recognition, Character recognition License plate recognition.
This project is talking about the digital character recognition simulation demonstration system.It mainly use Least error of Bayes method and Least risk of Bayes method under the Normal Distribution to realize handwritten figures recognition from 0 to 9. First, handwritten digitals should be simulated;then, extract the sample characteristics by useing 5*5 templates，using templates can get feature what not very difference from samples what have the same appearance and different size,so the samples what have the same appearance and different size would be divided to similar
;at last,combine with Bayes method to recognition , this two kinds of methods avoided a lot of classification errors and attaining the wrong probability to be smallest.
This design realized by using Visual C++ 6.0, experimental evidence , this system for any handwritten figures could correctly identify.But for the relatively small handwritten figures ,miscalculated the probability will be greater,the reason is that the sample is limited,usually,the number of samples to the number of features from five to ten times.
Key :Least error,Least risk, Feature selection，Handwritten simulation,
MFC
