@article{pub5227,
    abstract = {In this paper, we present a recognition system for on-line handwritten texts acquired from a whiteboard. The system is based on the combination of several individual classifiers of diverse nature. Recognizers based on different architectures (hidden Markov models and bidirectional long short-term memory networks) and on different sets of features (extracted from on-line and off-line data) are used in the combination. In order to increase the diversity of the underlying classifiers and fully exploit the current state-of-the-art in cursive handwriting recognition, commercial recognition systems have been included in the combined system, leading to a final word level accuracy of 86.16%. This value is significantly higher than the performance of the best individual classifier (81.26%). },
    year = {2009},
    title = {Combining diverse systems for handwritten text line recognition},
    note = {10.1007/s00138-009-0208-9},
    journal = {Machine Vision and Applications (MVA)},
    volume = {online},
    pages = {1-13},
    publisher = {Springer Berlin / Heidelberg},
    author = {Marcus Liwicki and Horst Bunke and James Pittman and Stefan Knerr},
    url = {http://dx.doi.org/10.1007/s00138-009-0208-9}
}