Even digital content is widely used nowadays, printed documents are still ubiquitously accepted and circulated. Therefore, identifying the printed character source is essential for criminal investigations to authenticate the digital copies of the printed documents. This study carefully examines the important statistical features from Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, and Gabor filter to identify the printer source for Chinese characters by using support vector machine (SVM) and decision fusion of feature selection. Even the subject of printed Chinese character source identification has been investigated, the proposed technique further expands the feature space which achieves superior experimental results and outperforms the techniques described in the literatures. Therefore, the methodology proposed in this study can accomplish high classification accuracy rate which show promising applications for real world digital forensics.