Lexical post-processing optimization for handwritten word recognition

This paper presents a lexical post-processing optimization for handwritten word recognition. The aim of this work is to explore the combination of different lexical post-processing approaches in order to optimize the recognition rate, the recognition time and memory requirements. The present method focuses on the following tasks: a lexicon organization with word filtering, based on holistic word features to deal with large vocabulary (creation of static sublexicon compressed in a tree structure); a dedicated string matching algorithm for online handwriting (to compensate for the recognition and the segmentation errors); and a specific exploration strategy of the results provided by the analytical word recognition process. Experimental results are reported using several lexicon sizes (about 1000, 7000 and 25000 entries) to evaluate different optimization strategies according to the recognition rate, computational cost and memory requirements.