Combining diverse systems for handwritten text line recognition

Marcus Liwicki; Horst Bunke; James Pittman; Stefan Knerr
In: Machine Vision and Applications (MVA), Vol. online, Pages 1-13, Springer Berlin / Heidelberg, 2009.


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%).



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