中文草書書法字帖的文字切割與辨識

Abstract

書法是我們中國國粹之一，而草書是書法字型中變化極為複雜的一種字型。在這篇論文中，我們設計了一個草書書法字帖的文字自動切割與辨識工具。如此一來，便能夠將草書書法字完整保存下來。我們的系統的輸入影像為二值化的書法字帖點陣圖像。文字切割與文字辨識是我們的系統中兩個主要的模組。 在文字切割模組方面，我們對輸入影像建構了一個最短距離的對應圖，這個對應圖紀錄了影像中每一個點的最短路徑。接下來利用這個對應圖並配合垂直投影將可以找到垂直行文字的切割路徑。再同樣的對每一個垂直行文字建構最短距離的對應圖找到初始的水平文字切割路徑，最後利用限制切割路徑與中文草書書法字的特性來去除掉多餘的水平文字切割路徑。 在文字辨識模組方面，我們希望找到合適草書書法字使用的文字辨識核心。我們考慮了四種不同的統計式文字特徵抽取方法：邊緣方向數(contour direction counts)、通過筆劃數(crossing counts)、Oka’s cellular features 和 peripheral background area features。利用這四種文字特徵抽取方式的各種不同權重的組合及五種計算特徵距離的方式，找出對於我們的實驗影像有最高辨識率的組合。 我們的實驗影像是從五位中國古代的書法家的草書作品中選出了五十五張的字帖影像。在垂直行文字的切割成功率有98.23%，而在水平文字切割的正確率是84.06%。

The calligraphy is one of the quintessence of Chinese culture. The Chinese cursive script is a quite complicated style in calligraphy script styles. In this thesis, we design an automatic segmentation and recognition tool for Chinese characters in cursive script. Thus, we can preserve the Chinese characters in cursive script in a database. The input of our system is binary Chinese cursive script calligraphy image without noises. Our system contains two major modules: characters segmentation and characters recognition. In the characters segmentation module, we first construct a shortest distance map that contains each shortest path for each point of the input image. Then the shortest distance map is combined with the vertical projection to find the vertical text line segmentation paths. Next, we apply the shortest distance map in each text line to obtain initial horizontal character segmentation paths. Finally, we reduce the horizontal character segmentation paths by using the path constraints and cursive script features. In the characters recognition module, we design a good OCR engine that has a high recognition rate for Chinese characters in cursive script. We use four statistical features: contour directional features, crossing count features, Oka's cellular features and Peripheral background area features. These four features are measured with five feature distance measurements to select the OCR kernel with the highest recogniztion rate of our testing characters in cursive script. In our experiments, we select 55 calligraphy images from five different authors. The success rates are 98.23% in vertical text line segmentation, and 84.06% in horizontal character segmentation.