Abstract: Writer adaptation is the process of converting a generic (writer-independent) handwriting recognizer into a personalized (writer-dependent) recognizer with improved accuracy for a particular user. While training the generic recognizer uses large amounts of data from several writers, the adaptation process uses only a few samples from a single user. In this paper we present a) an automatic approach for identifying allographs (character shapes/styles) from handwritten characters through clustering, b) a novel architecture for a personalizable recognizer that utilizes allograph information, and c) a kernel based approach for personalizing the recognizer. Using the new approach, personalization results with twenty one users indicate that handwritten single character recognition errors can be reduced by over 24% (or 41%) using as few as 5 (or 15) samples.