Local Feature Based Online Mode Detection with Recurrent Neural Networks
Sebastian Otte, Dirk Krechel, Marcus Liwicki, Andreas R. Dengel · 2012
In this paper we propose a novel approach for online mode detection, where the task is to classify ink traces into several categories. In contrast to previous approaches working on global features, we introduce a system completely relying on local features. For classification, standard recurrent neural networks (RNNs) and the recently introduced long short-term memory (LSTM) networks are used. Experiments are performed on the publicly available IAMonDo-database which serves as a benchmark data set for several researches. In the experiments we investigate several RNN structures and classification sub-tasks of different complexities. The final recognition rate on the complete test set is 98.47% in average, which is significantly higher than the 97% achieved with an MCS in previous work. Further interesting results on different subsets are also reported in this paper.