Adaptation of an address reading system to local mail streams

A scheme for handwriting adaptation for post offices is described to improve recognition performance of German addresses. The recognition system is based on a tied-mixture hidden Markov model, whose parameters are updated using the expectation maximization technique, the maximum likelihood linear regression algorithm and a new discriminative adaptation technique, the scaled likelihood linear regression. Contrary to the usual approach of adapting a writer-independent system to a specific writer we propose to adapt the system to the writer-independent data of a specific post office. The resulting system for each post office yields up to 16% lower word recognition errors.