臺灣博碩士論文加值系統

本篇論文提出基本與複合的特徵，並使用我們提出之SEART類神經網路為
分類器以辨識手寫中文字。基本特徵是從區域與整體的角度來抽取，具有
非常穩定的優點。因為手寫中文字的變異性很大，因此在抽取複合特徵時
，我們加入模糊的觀念來抽取這樣的結構性特徵。我們所抽取的兩類特徵
結合了統計性與結構性的優點，在穩定性與代表性上都有很好的表現；而
且，這兩種特徵可以在一回合中抽取完成，所以計算負荷並不會太大。在
辨識方面，我們使用稱之為延伸監督式類神經網路(SEART)作為分類器，
以辨識出正確的中文字類別。SEART具有快速，良好的一般化能力與處理
例外等優點，即使是複雜的問題，也有很不錯的分類結果。針對手寫中文
字的特性，我們使用模糊觀念抽取特徵並以類神經網路為分類器，這對於
雜訊，變異性及扭曲等情形都具有良好的容忍能力。最後，我們採用電通
所建立的手寫中文字庫(CCL/HCCR3)，以605個小學課本常用字為實驗，獲
得平均90.24%的辨識結果。由此，我們證實所提系統的可行性，即使有嚴
重的扭曲或變異，應用模糊觀念及類神經網路在手寫中文辨識上仍有很好
的表現。

A handwritten Chinese characters recognition method using SEART
neural network model with primitive and compound fuzzy features
is proposed. The primitive features are extracted in local and
global view. Also they have good stability. Since the
writings of handwritten Chinese characters vary a lot, we adopt
fuzzyract the compound features in structural view. These
categories of features are extracted in one pass, so thel
effort is not heavy. We combine the two categories of features
and use a fast classifier, named supervised extended ART
(SEART) neural network model, to recognize the handwritten
Chinese characters. The SEART classifier has excellent
performance, fast, good generalization and exceptions handling
ability in complex problems. Using the fuzzy set theory in
features extraction and the neural network as a classifier are
helpful for tolerating distortions, noises and variations. In
spite of the poor thinning, an average of 90.24% recognition
rate on the 605 test characters is obtained. The database used
is HCCRBASE (provided by CCL, ITRI, Taiwan). It not only
confirms the feasibility of the proposed system, but also
suggests that applying the fuzzy set theory and neural networks
on HCCR is an efficient and promising approach.