In this paper we propose a technique of correcting online handwriting recognition errors using morphemic lexicons and a set of confusion lists for mistaken characters collected from classification errors. Given a string of Hangeul characters returned by a baseline recognizer, we raise a set of alternative characters out of a confusion dictionary, one for each character in the returned string. Then we conduct a post-processing via a dynamic programming-based lattice search for a sequence of legal words while consulting morphemic lexicons. The proposed methods can handle short broken phrases in natural online handwriting of sentences. Experiment shows that, for handwriting strings of 3.09 characters long on average, the proposed method improved the phrase recognition accuracy from 55.43% before post-processing to 86.63%, an error reduction of 70.00%, with single hypotheses.