Script identification is significant for attaining information from document images. Most algorithms on texture feature extraction from document images for script identification are inadaptable to the skew of text line presently. For the skew of text line is inevitably， a new algorithm robust to the skew of text line is proposed. Steerable Pyramid transform is used on the document images and the energy statistical features of sub-bands is extracted. Through the realignment of features， the algorithm implements robustness to rotation. Libsvm is used as a classifier. The experiments are conducted on image database containing ten scripts that are scanned from books or magazines. The test samples are rotated with different angles and the results confirm that the algorithm can identify scripts accurately and is robust to the skew of text line simultaneously.