Offline grammar-based recognition of handwritten sentences.
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.
Zimmermann
Matthias
M
International Computer Science Institute, Berkeley, CA 94704-1198, USA. zimmerma@icsi.berkeley.edu
Chappelier
Jean-Cédric
JC
Bunke
Horst
H
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