A script identification method is proposed based on Gaussian derivative filter bank. The texture characteristic of document images is analyzed. Compared with traditional wavelet transform, the proposed algorithm can extract edge and ridge features with more orientations. The support vector machine (SVM) is applied for training and classifying the extracted features to identify scrip ts in different languages. Experiments are performed upon document images with ten kinds of languages (including Chinese, Russian, English, Japanese, Korean, Arabic, etc). The effects of different Gaussian derivative filter parameters on the identification performance are tested, and other three script identification methods based on texture are selected for comparing. Experimental results show that the proposed algorithm can improve the speed and the correct rate of script identification.