Hybrid HMM/ANN Models for Bimodal Online and Offline Cursive Word Recognition

Espana-Boquera S., Gorbe-Moya J., Zamora-Martinez F., Castro-Bleda M. J.

20th International Conference on Pattern Recognition Conference, İstanbul, Turkey, 23 - 26 April 2010, vol.6388, pp.14-15 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 6388
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.14-15
  • Istanbul Technical University Affiliated: No


The recognition performance of current automatic offline handwriting transcription systems is far from being perfect. This is the reason why there is a growing interest in assisted transcription systems, which are more efficient than correcting by hand an automatic transcription. A recent approach to interactive transcription involves multimodal recognition, where the user can supply an online transcription of some of the words. In this paper, a description of the bimodal engine, which entered the "Bi-modal Handwritten Text Recognition" contest organized during the 2010 ICPR, is presented. The proposed recognition system uses Hidden Markov Models hybridized with neural networks (HMM/ANN) for both offline and online input. The N-best word hypothesis scores for both the offline and the online samples are combined using a log-linear combination, achieving very satisfying results.