An essential step in character recognition is to extract the skeleton characteristics of the character. In this paper, an efficient algorithm is proposed to extract visually satisfactory skeleton from printed and handwritten characters, which overcomes fundamental shortcomings of our previous skeletonization technique based on the maximum modulus symmetry of wavelet transform (WT). The proposed method is motivated from some desirable properties of the WT with constructed wavelet functions: namely, the local modulus minima of the WT are scale-independent at different level scales and are located at the medial axis of the symmetrical contours of character stroke. Thus the modulus minima of the WT are computed as the intrinsic skeletons of character strokes. To achieve faster implementation, a multiscale processing technique is employed. Thus major structures of the skeleton are extracted using the coarse scale, while fine structures are extracted using the fine scale. We have tested the algorithm on handwritten and printed character images. Experimental results show that the proposed algorithm is applicable to not only binary image but also gray-level image where it can be impractical to use other skeletonization techniques, such as thinning and distance transforms. Further, it can effectively remove unwanted artifacts and branches from the extracted skeletons at the intersections and junctions of character strokes and is robust against noises while most existing methods perform poorly. © World Scientific Publishing Company.