Combining On-Line and Off-Line Systems for Handwriting Recognition

In this paper we present a new multiple classifier system (MCS)for recognizing notes written on a whiteboard. This MCS combines one off-line and two on-line handwriting recognition systems derived from previous work. The recognizers are all based on Hidden Markov Models but vary in the way of preprocessing and normalization. To combine the output sequences of the recognizers, we incrementally align the word sequences using a standard string matching algorithm. For deriving the final decision a voting strategy is applied. With the combination we could increase the system performance over the best individual recognizer by about 2%.