Keyword Spotting in Online Handwritten Documents Containing Text and Non-text Using BLSTM Neural Networks

By Emanuel Indermühle, Volkmar Frinken, Andreas Fischer, Horst Bunke. 2011

11 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Urgen Schmidhuber
2. Volkmar Frinken
3. Horst Bunke
4. Alex Graves
5. Emanuel Indermühle
6. Yoshua Bengio
7. David R Ha
8. Quoc V. Le

In Computer Vision and Pattern Recognition

1. J. Urgen Schmidhuber
2. Volkmar Frinken
3. Emanuel Indermühle
4. Alex Graves
5. Horst Bunke
6. Luc Van Gool
7. Yoshua Bengio
8. Dacheng Tao

Citations in the corpus

• Deep learning in neural networks: An overview (2014, methodology): “ prediction. On the IAM-OnDoDB benchmark, LSTM RNNs (Sec. 5.13) outperformed all other methods (HMMs, SVMs) on online mode detection (Otte et al., 2012; Indermuhle et al., 2012) and keyword spotting (Indermuhle et al., 2011). On the long time lag problem of language modelling, LSTM RNNs outperformed”
• Mode Detection in Online Handwritten Documents Using BLSTM Neural Networks (2012, methodology): “The flexibility of this system is also demonstrated by its application to keyword spotting [3, 10].”
• Understanding LSTM - a tutorial into Long Short-Term Memory Recurrent Neural Networks (2019, unknown): “In 2012 BSLTM was applied to keyword spotting and mode detection distinguishing different types of content in handwritten documents, such as text, formulas, diagrams and figures, outperforming HMMs and SVMs [44, 45, 59].”