Neural network language models for off-line handwriting recognition
By Francisco Zamora-Martínez, Volkmar Frinken, Salvador España Boquera, María José Castro Bleda, Andreas Fischer, Horst Bunke. 2014
79 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
- Greg Van Houdt
- Carlos Mosquera
- Gonzalo Nápoles
- J. Urgen Schmidhuber
- David R Ha
- Alex Graves
- Sergey Levine
- Yoshua Bengio
In Computer Vision and Pattern Recognition
- Greg Van Houdt
- Carlos Mosquera
- Gonzalo Nápoles
- J. Urgen Schmidhuber
- Luc Van Gool
- Xiaogang Wang
- Andrew Zisserman
- Trevor Darrell
Citations in the corpus
- Deep learning in neural networks: An overview (2014, methodology): “DP algorithms are also essential for systems that combine concepts of NNs and graphical models, such as Hidden Markov Models (HMMs) (Baum & Petrie, 1966; Stratonovich, 1960) and ExpectationMaximization (EM) (Dempster, Laird, & Rubin, 1977; Friedman, Hastie, & Tibshirani, 2001), e.g., Baldi and Chauv”
- A review on the long short-term memory model (2020, background): “Zhang et al. (2018) explored the effects of attention in convolutional LSTM with regards to gesture recognition, and discovered that convolutional structures in the gates do not play the role of spatial attention.”