The disclosed architecture is a new feature extraction approach to handwriting recognition. Given an handwriting sample (e.g., from an online source), a sequence of time-ordered dominant points are extracted, which include stroke-endings, points corresponding to local extrema of curvature, and points with a large distance to the chords formed by pairs of previously identified neighboring dominant points. At each dominant point, a multi-dimensional feature vector is extracted, which includes a combination of coordinate features, delta features, and double-delta features.