Conventional Gabor representation and its extracted features often yield a fairly poor performance in extracting the invariance features of objects. To address this issue, a global Gabor representation method for raised characters pressed on label is proposed in this paper, where the representation only requires few summations on the conventional Gabor filter responses. Features are then extracted from these new representations to construct the invariant features. Experimental results clearly show that the obtained global Gabor features provide good performance in rotation, translation, and scale invariance. Also, they are insensitive to illumination conditions and noise changes. It is proved that Gabor filters can be reliably used in low-level feature extraction in image processing and the global Gabor features can be used to construct robust invariant recognition system.