Writer Adaptation for Online Handwriting Recognition

  • Brakensiek A
  • Kosmala A
  • Rigoll G
  • 14


    Mendeley users who have this article in their library.
  • 3


    Citations of this article.


In this paper an on-line handwriting recognition system with focus on adaptation techniques is described. Our Hidden Markov Model (HMM) -based recognition system for cursive German script can be adapted to the writing style of a new writer using either a retraining depending on the maximum likelihood (ML)-approach or an adaptation according to the maximum a posteriori (MAP)-criterion. The performance of the resulting writer-dependent system increases significantly, even if only a few words are available for adaptation. So this approach is also applicable for on-line systems in hand-held computers such as PDAs. This paper deals with the performance comparison of two different adaptation techniques either in a supervised or an unsupervised mode with the availability of different amounts of adaptation data ranging from only 6 words up to 100 words per writer.

Author-supplied keywords

  • adaptation of HMMs
  • online cursive handwriting recognition
  • writer independent

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Anja Brakensiek

  • Andreas Kosmala

  • Gerhard Rigoll

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free