5 Conclusions

In this paper we have presented the NPen ++ system, a neural recognizer for writer dependent and writer independent on-line cursive handwriting recognition. This system combines a robust input representation, which preserves the dynamic writing information, with a neural network integrating recognition and segmentation in a single framework. This architecture has been shown to be well suited for handling temporal sequences as provided by this kind of input. Evaluation of the system on dierent tasks with vocabulary sizes ranging from 400 to 20,000 words has shown recognition rates from 92.9% to 84.1% in the writer independent case and from 98.6% to 88.9% in the writer dependent case. These results are especially promising because they were achieved with a small training set compared to other systems (e.g. [3]). As can be seen in Table 1, the system has proved to be virtually independent of the vocabulary. Though the system was trained on rather small vocabularies (e.g. 400 words in the writer dependent system), it generalizes well to completely dierent and much larger vocabularies.