本文的目的在於建立一套文字辨識系統，提出新的觀點以單一字元為基礎，利用切割演算法提升系統的效率。本系統包含了兩個部分，切割字元模組與辨識模組。 本研究提出的切割字元模組，模擬中文字型的特性，利用切割演算法將一個中文字的結構切割成為兩個字旁的區塊。辨識模組則是使用總像素(Total pixel count)、筆劃穿越數(crossing count features)以及周圍的背景區域(Peripheral background area features)，利用此三種特徵值來做六個階段的特徵篩選，進而由資料庫中得到候選名單。最後經由樣本比對的方法，找出候選名單中與文字影像最相似的結果 最後分別測試典型辨識系統與加了切割式系統的差異，在效率方面後者明顯的快出了許多。 In this paper, we purpose to construct a printed Chinese OCR system by segmenting an optical character, which included character segmentation kernel and character recognition kernel. Character segmentation kernel segments Chinese character into two parts by the distinctions of Chinese. Character recognition kernel achieves 6-layers feature filters by three character features, which are total pixel count feature, crossing count feature and peripheral background area feature. After these feature filters processed, the system will evaluate the remaining candidate characters by template matching. Our experiment shows that the OCR with segmentation method has better performance on the template-matching stage.
