PT Unknown
AU Andreas Fischer
   Volkmar Frinken
   Horst Bunke
   Ching Y. Suen
TI Improving HMM-Based Keyword Spotting with Character Language Models
BT 12th International Conference on Document Analysis and Recognition
PY 2013
BP 506
EP 510
DI 10.1109/ICDAR.2013.107
AB Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance.
ER