Local Feature Based Online Mode Detection with Recurrent Neural Networks

• Published in: 2012 International Conference on Frontiers in Handwriting Recognition pp. 533 - 537
• Main Authors: Otte, S., Krechel, D., Liwicki, M., Dengel, A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2012
• Subjects: Feature extraction; Gesture Recognition; Handwriting recognition; Local Features; Long Short-Term Memory; LSTM; Mode Detection; Neural Networks; Recurrent neural networks; RNN; Sequence Classification; Sequence Learning; Shape; Training
• ISBN: 9781467322621; 1467322628
• DOI: 10.1109/ICFHR.2012.229

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.