A HMM–based System for Recognition of Handwritten Address Words

This paper introduces a new handwriting recognition system that is currently under development. Our application is the reading of German handwritten addresses for automatic mail sorting. The quality of the handwritten words is often bad in this application, because writers are not very cooperative. Therefore we have developed some suitable and efficient preprocessing operations to clean the image and normalize the writing.

Because the words are often difficult to segment into letters, we have chosen a segmentation-free approach for recognition with semi-continuous Hidden Markov Models. We are applying the technique of context modelling in a model hierarchy in order to train more specific letter models.

For training and evaluation, we have used a large sample of 15000 handwritten city and street names. A number of experiments have been performed to evaluate strategies for feature space reduction (Karhunen-Loeve transform, linear discriminant analysis). On a 100 word lexicon, we achieve recognition rates of up to 90% on large independent test sets.