Age characterization from handwriting (HW) has important applications as it may allow distinguishing normal HW evolution due to age from ab-normal HW change, potentially related to a cognitive decline. We propose, in this work, an original approach for online HW style characterization based on a two-level clustering scheme. The first level allows generating writer-independent word clusters according to raw spatial-dynamic HW information. At the second level, the writer words are converted into a Bag of Prototype Words that is augmented by a measure of his/her writing stability across words. For age characterization, we harness the two-level HW style representation us-ing unsupervised and supervised schemes, the former aiming at uncovering HW style categories and their correlation with age and the latter at predicting age groups. Our experiments on a large database show that the two level representa-tion uncovers interesting correlations between age and HW style. The evalua-tion is based on entropy-based information theoretic measures to quantify the gain on age information from the proposed two-level HW style representation