High-Performance OCR for Printed English and Fraktur Using LSTM Networks
By Thomas Breuel, Adnan Ul-Hasan, M. A. Azawi, Faisal Shafait. 2013
260 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
- Rizwan Ahmed Khan
- J. Urgen Schmidhuber
- Maira Sami
- Jamshed Memon
- David R Ha
- Alex Graves
- Sergey Levine
- Yoshua Bengio
In Computer Vision and Pattern Recognition
- Rizwan Ahmed Khan
- Maira Sami
- Jamshed Memon
- J. Urgen Schmidhuber
- Luc Van Gool
- Xiaogang Wang
- Andrew Zisserman
- Trevor Darrell
Citations in the corpus
- Deep learning in neural networks: An overview (2014, background): “For optical character recognition (OCR), LSTM RNNs outperformed commercial recognizers of historical data (Breuel et al., 2013).”
- Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR) (2020, background): “ten documents, primarily based on deep learning [20] approach. This paradigm shift has been sparked due to adaption of cluster computing and GPUs and better performance by deep learning architectures [21], which includes Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN), Long Short”
- Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review (2018, unknown): “, 2014), automatic recognition of characters (Breuel et al., 2013), automatic generation of subtitles (Zaremba et al.”