Lexicon Reduction Based on Global Features for On-line Handwriting

Handwriting Recognition generally uses a dictionary : in the case of large vocabularies, our system helps checking the existence of the words provided by the recognizer, or when there is a model to go through per word, it will reduce the number of models to go through. In the case of smaller vocabularies it can be used as a recognizer. From the signal provided by a digitizing tablet we build a global silhouette representing a word in a very simple and very compact way. After having made a statistical study about the possible silhouettes of each character in the alphabet and their frequency, we use these to build all the possible silhouettes to associate with each lexicon word and their corresponding probabilities. The lexicon is organized as an "epigenetic" neural network. In the recognizing phase, when retrieving words corresponding to a given silhouette, this network will let us reach not only the words having been describred with this silhouette, but also those possessing a similar one and not having been described by it. The result is a list of sorted words ranked according to the relevance of this silhouette feature for each word. We also show how to combine different kinds of features with such networks. We next show how to introduce a confidence rate in the word outline by using continuous features. The theoretical results are very encouraging. Measurements are in progress on on-line texts.