http://orcid.org/0000-0002-8013-8645
References
- Bassil, Y., and M. Alwani. 2012. OCR post-processing error correction algorithm using google online spelling suggestion. arXiv Preprint arXiv:1204.0191.
- Bissacco, A., M. Cummins, Y. Netzer, and H. Neven. 2013. PhotoOCR: Reading text in uncontrolled conditions. In Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia. pp. 785–92.
- Gomaa, W. H., and A. A. Fahmy. 2013. A survey of text similarity approaches. International Journal of Computer Applications 68 (13). doi:10.5120/11638-7118.
- International Telecommunication Union (ITU). (2009). International Morse code. ITU-R M.1677-1. Accessed October, 2009. https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.1677-1-200910-I!!PDF-E.pdf.
- Jaderberg, M., A. Vedaldi, and A. Zisserman. 2014. Deep Features for Text Spotting, 512–28. Cham: Springer International Publishing.
- Kasampalis, S. 2015. Mastering Python design patterns. Packt Publishing Ltd, Birmingham, UK.
- LeCun, Y., C. Cortes, and C. J. Burges. 1998. The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/.
- McCallum, J. 2014–2016. Autocorrect: 0.2.0. https://pypi.python.org/pypi/autocorrect/0.2.0.
- Morris, T., A. Dovev, S. Weil, and Zdenop. 2016. ImproveQuality tesseract-ocr/tesseract Wiki GitHub. Accessed June 07, 2016. https://github.com/tesseract-ocr/tesseract/wiki/ImproveQuality.
- Radford, A. 2014. Introduction to deep learning with python. http://www.slideshare.net/indicods/deep-learning-with-python-and-the-theano-library.
- Silberpfennig, A., L. Wolf, N. Dershowitz, S. Bhagesh, and B. B. Chaudhuri. 2015, August. Improving OCR for an under-resourced script using unsupervised word-spotting. In Document Analysis and Recognition (ICDAR), 2015 13th International Conference on, Nancy, France. pp. 706–10.
- Smith, R. 2007. An overview of the tesseract OCR engine. 2013 12th International Conference on Document Analysis and Recognition 2, Washington D.C., USA 629–33.
- Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15 (1):1929–58.
- Ye, Q., and D. Doermann. 2015. Text detection and recognition in imagery: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (7, July):1480–500. doi:10.1109/TPAMI.2014.2366765.
- Zhang, X., J. Zhao, and Y. LeCun. 2015. Character-level convolutional networks for text classification. In Advances in neural information processing systems 28, Eds. C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, 649–57. Curran Associates, Inc., Montreal, Canada.