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 sub-lexicon compressed in a trie structure); a dedicated string matching algorithm for on-line handwriting (to compensate 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 1,000; 7,000 and 25,000 entries) to evaluate different optimization strategies according to the recognition rate, computational cost and memory requirements.