Improvements in RWTH's System for Off-Line Handwriting Recognition

Abstract

In this paper we describe a novel HMM-based system for off-line handwriting recognition. We adapt successful techniques from the domains of large vocabulary speech recognition and image object recognition: moment-based image normalization, writer adaptation, discriminative feature extraction and training, and open-vocabulary recognition. We evaluate those methods and examine their cumulative effect on the recognition performance. The final system outperforms current state-of-the-art approaches on two standard evaluation corpora for English and French handwriting.