Npen++: An On-Line Handwriting Recognition System. This paper presents the on-line handwriting recognition system NPen++ developed at the University of Karlsruhe and the Carnegie Mellon University. The NPen++ recognition engine is based on a Multi-State Time Delay Neural Network and yields recognition rates from 96% for a 5000 word dictionary to 93.4% on a 20,000 word dictionary and 91.2% for a 50,000 word dictionary. The proposed tree search and pruning technique reduces the search space considerably without loosing too much recognition performance compared to an exhaustive search. This allows running the NPen++ recognizer in real-time with large dictionaries.

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  1. Sternby, Jakob; Morwing, Jonas; Andersson, Jonas; Friberg, Christer: On-line arabic handwriting recognition with templates (2009)
  2. Bahlmann, Claus: Directional features in online handwriting recognition (2006) ioport
  3. Bahlmann, Claus: Advanced sequence classification technique applied to online handwriting recognition. (2005)
  4. Cho, Sung-Jung; Kim, Jin H.: Bayesian network modeling of strokes and their relationships for on-line handwriting recognition. (2004)
  5. Hu, Jianying; Lim, Sok Gek; Brown, Michael K.: Writer independent on-line handwriting recognition using an HMM approach. (2000) ioport