Parsing N-best lists of handwritten sentences

This paper investigates the application of a probabilistic parser for natural language on the list of the N-best sentences produced by an offline recognition system for cursive handwritten sentences. For the generation of the N-best sentence list an HMM-based recognizer including a bigram language model is used. The parsing of the sentences is achieved by a bottom-up chart parser for stochastic context-free grammars which produces the parse tree of the input sentence as well as the word tags. From a collection of corpora we extract the linguistic resources to build the lexicon, a word bigram model and the stochastic context-free grammar. Results from experiments indicate an increase of the word and sentence recognition rate when using the proposed combination scheme.