A Chinese Font Recognition Method Based on Empirical Mode Decomposition
基于经验模式分解的汉字字体识别方法

This paper gives a novel approach to recognize Chinese fonts based on Empirical Mode Decomposition (EMD). By analyzing and comparing a great number of Chinese characters, 8 basic strokes are selected to characterize the structural attributes of Chinese fonts. Based on them, stroke feature sequences of each text block are calculated. Once decomposed by EMD, their first two intrinsic mode functions (IMFs), which are of the highest frequencies, are used to calculate the stroke energy of all the 8 basic strokes, forming the average of the energy of the two IMFs over the length of the sequence. To distinguish bold fonts from their regular fonts, average of the pixel's gray levels of the text is calculated and appended to the feature vector to form a 9 dimensional feature. Finally, the minimum distance classifier is used to recognize the fonts. Experiments show encouraging recognition rates.