Parsing N-Best Lists of Handwritten Sentences

This paper investigates the application of a probabilistic parser for natural language on the list of the Nbest sentences produced by an off-line 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.

• Type: Conference
• Year: 2003
• Where: ICDAR
• Authors: Matthias Zimmermann, Jean-Cédric Chappelier, Horst Bunke
• Keywords: Cursive Handwritten Sentences, Document Analysis, ICDAR 2003, Sentence Recognition Rate, Stochastic Context-free Grammar