Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICFHR.2012.229 | Frontiers in Handwriting Recognition |
Keywords | Field | DocType |
recurrent neural networks,local feature,different complexity,classification sub-tasks,rnn structure,available iamondo-database,benchmark data,previous work,online mode detection,complete test set,final recognition rate,different subsets,previous approach,handwriting recognition,image classification,feature extraction | Pattern recognition,Computer science,Recurrent neural network,Gesture recognition,Handwriting recognition,Feature extraction,Artificial intelligence,Contextual image classification,Artificial neural network,Sequence learning,Machine learning,Test set | Conference |
ISSN | ISBN | Citations |
2167-6445 | 978-1-4673-2262-1 | 14 |
PageRank | References | Authors |
0.70 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Otte | 1 | 47 | 12.57 |
| Dirk Krechel | 2 | 44 | 13.19 |
| Marcus Liwicki | 3 | 1292 | 101.35 |
| Andreas Dengel | 4 | 1926 | 280.42 |