使用模糊系統區別文件中手寫字與印刷字之研究

Abstract

手寫字與印刷字的辨識在光學文字辨識及文件分析的領域中扮演著一個重要的角色。 為了增進光學文字辨識系統的辨識率，被辨識文字的類型必須事先知道是屬於手寫字或者 是印刷字。 在這篇論文中，一個用來區別手寫字與印刷字的模糊分類系統將被建立。 而為了提昇此分類系統的正確率，敏感度分析的方法被用來挑選較有效的特徵以用來 區別手寫字與印刷字。 然後模糊邏輯的觀念將被引進以整合這些特徵，進而完成此模糊分類系統。 而這裡所提出的方法將可被應用在中文及英文的文件上。 經由實驗的驗證，顯示此分類系統具有不錯的分類結果，甚至對於手寫字與印刷字交雜 的情況亦是如此。

The handwritten and printed character recognition plays an important rule in the area of the optical character recognition (OCR) and the document analysis. In order to promote the performance of the OCR system, the character style in handwritten or in printed need to be known in advance. In this thesis, a fuzzy classification system which can discriminate handwritten and printed characters is implemented. In order to promote the correct rate of the classification system, the most useful features of the classification system are extracted by using the sensitivity analysis. Then these features are combined by using the concept of the fuzzy logic. The proposed method can apply to the Chinese and the English document images. Moreover, the experimental results show that the classification system has a good performance even in the document which is mixed handwritten and printed characters.