Publication
Combining On-Line and Off-Line Bidirectional Long Short-Term Memory Networks for Handwritten Text Line Recognition
Marcus Liwicki; Horst Bunke
In: Proc. 11th Int. Conference on Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR-2008), 11th, August 19-21, Montreal, QC, Canada, Pages 31-36, 2008.
Abstract
In this paper we present a multiple classifier system (MCS) for on-line handwriting recognition. The MCS combines several individual recognition systems based on bidirectional long short-term memory networks. To obtain diverse recognizers, we use different feature sets based on on-line and off-line features. Furthermore, we generate a number of different recognizers by changing the initialization of the networks. To combine the word sequences output by the recognizers, we incrementally align these sequences using the recognizer output voting error reduction framework (ROVER). For deriving the final decision, different voting strategies are applied. The best combination ensemble has a recognition rate of 83.64%, which is significantly higher than the 81.26% achieved by the best individual classifier.