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, Türkiye, 23 - 26 Nisan 2010, cilt.6388, ss.14-15 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 6388
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.14-15
  • İstanbul Teknik Üniversitesi Adresli: Hayır

Özet

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.