The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks
Abstract
Multidimensional long short-term memory recurrent neural networks achieve impressive results for handwriting recognition. However, with current CPU-based implementations, their training is very expensive and thus their capacity has so far been limited. We release an efficient GPU-based implementation which greatly reduces training times by processing the input in a diagonal-wise fashion. We use this implementation to explore deeper and wider architectures than previously used for handwriting recognition and show that especially the depth plays an important role. We outperform state of the art results on two databases with a deep multidimensional network.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
Cited by
TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models
Li, M., Lv, T., Chen, J., Cui, L., Lu, Y., Florencio, D., Zhang, C., Li, Z., & Wei, F. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26538 · Cited by 573
Benchmarking large language models for handwritten text recognition
Crosilla, G., Klic, L., & Colavizza, G. (2025) · Journal of Documentation · doi:10.1108/jd-03-2025-0082 · Cited by 34
Metadata
- Authors
- Voigtlaender, P., Doetsch, P., & Ney, H.
- Venue
- 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)
- Publisher
- IEEE
- Published
- 2016-10
- Volume
- 2016
- Pages
- 228-233
- OpenAlex
- W2573601984
- Cited by
- 211
- References
- 6
- FWCI
- 6.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Voigtlaender, P., Doetsch, P., & Ney, H. (2016). Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks. 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). https://doi.org/10.1109/icfhr.2016.0052
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