title: Offline grammar-based recognition of handwritten sentences.
abstract: This paper proposes a sequential coupling of a Hidden Markov Model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using Stochastic Context-Free Grammars (SCFG) extracted from a text corpus. Based on extensive experiments, we conclude that syntax analysis helps to improve recognition rates significantly.
authors.affiliation: International Computer Science Institute, Berkeley, CA 94704-1198, USA. zimmerma@icsi.berkeley.edu
authors.forename: Matthias
authors.initials: M
authors.lastname: Zimmermann
authors.forename: Jean-Cédric
authors.initials: JC
authors.lastname: Chappelier
authors.forename: Horst
authors.initials: H
authors.lastname: Bunke
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D012660:Semantics