We propose a gray-scale character recognition method robust under the following difficulties of character recognition in video indexing; the supposed difficulties are binarization against a complex background and low resolution. Unlike a traditional character recognition scheme through image binarization, we directly extract Gabor features (called Gabor jets) from video contents. The use of the Gabor filters contributes to freeing a tricky binarization process for cluttered images, and furthermore provides localized directional edge features, which have phase-shift invariance to edge positions. To form a feature vector to be classified, we accumulate the extracted Gabor features along projection lines in local regions, and then categorize them with a standard LVQ classifier. The projective accumulation provides robustness under character deformation caused by variation of font types or imprecise segmentation. We compare the proposed method by experiments with a typical OCR method, for which correct binarization is advantageously given. The proposed method shows similar or superior performance to the other method in understanding video captions.