Abstract
Gregg shorthand is the most popular form of pen stenography in the United States. It has been adapted for many other languages. In order to substantially explore the potentialities of performing optical recognition of Gregg shorthand, we develop and present Gregg-1916, a dataset that comprises Gregg shorthand scripts of about 16 thousand common English words. In addition, we present a novel architecture for shorthand recognition which exhibits promising performance and opens up the path for various further directions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The dataset, together with our code, is made publicly available at https://github.com/anonimously/Gregg1916-Recognition.
- 2.
As Gregg shorthand is designed based on the word pronunciations instead of word spellings and that English word spellings are notoriously famous for the mismatch between the two, each letter may have multiple possible representations in the shorthand, which is actually designed according to the word pronunciations. Therefore, the binary classification task is highly non-trivial.
References
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281–305 (2012)
Encyclopaedia Britannica: Encyclopædia Britannica. Common Law, Chicago (2009)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Chollet, F., et al.: Keras (2015). https://keras.io
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. arXiv preprint (2016)
Gregg, J.R.: Gregg Shorthand Dictionary. Gregg Publishing Company, Upper Saddle River (1916)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3128–3137 (2015)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
KumarMishra, J., Alam, K.: A neural network based method for recognition of handwritten English Pitmans shorthand 102, 31–35 (2014)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics doklady, vol. 10, pp. 707–710 (1966)
Ma, Y., Leedham, G., Higgins, C., Myo Htwe, S.: Segmentation and recognition of phonetic features in handwritten Pitman shorthand 41, 1280–1294 (2008)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting On Association For Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310–1318 (2013)
Pedersoli, M., Lucas, T., Schmid, C., Verbeek, J.: Areas of attention for image captioning. In: ICCV-International Conference on Computer Vision (2017)
Rajasekaran, R., Ramar, K.: Handwritten Gregg shorthand recognition 41, 31–38 (2012)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Zhu, M., Chi, Z., Wang, X.: Segmentation and recognition of on-line Pitman shorthand outlines using neural networks, vol. 37, no. 5, pp. 2454–2458, December 2002
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhai, F., Fan, Y., Verma, T., Sinha, R., Klakow, D. (2018). A Dataset and a Novel Neural Approach for Optical Gregg Shorthand Recognition. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-00794-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00793-5
Online ISBN: 978-3-030-00794-2
eBook Packages: Computer ScienceComputer Science (R0)