Handwriting Recognition

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You can also find out about my published research on Google Scholar, which, as well as providing links to the papers and their abstracts, additionally provides the latest citation counts and index values.


These papers are the outcome of a collaboration between myself and three colleagues at Griffith University in Australia: Jolon Faichney, Michael Blumenstein and Trevor Hine.  Jolon was primarily responsible for the coding work, while Michael brought in the expertise with handwriting recognition and Trevor provided a brain science perspective from his work in psychology. While the practical focus of the work was on handwriting recognition, the deeper intention was to investigate the performance of various brain-inspired algorithms – particularly Jeff Hawkins’ hierarchical temporal memory architecture – on a task that the human brain handles relatively easily. This work expanded into the creation of Griffith University’s Cognitive Computing Unit (see the Brain-Inspired Algorithms page).


Thornton, J. R., Faichney, J., Blumenstein, M., Nguyen, V. & Hine, T. (2009). Offline Cursive Character Recognition: A state-of-the-art comparison. In: IGS 2009: Proceedings of the 14th Conference of the International Graphonomics Society, Dijon, France, pp. 148-151.


Thornton, J. R., Faichney, J., Blumenstein, M. & Hine, T. (2008). Character Recognition using Hierarchical Vector Quantization and Temporal Pooling. AI 2008: 21st Australasian Joint Conference on Artificial Intelligence. Lecture Notes in Computer Science, 5360, 562-572, Springer, ISSN 0302-9743.


Thornton, J. R., Gustafsson, T., Blumenstein, M. & Hine, T. (2006). Robust Character Recognition using a Hierarchical Bayesian Network. AI 2006: 19th Australian Joint Conference on Artificial Intelligence. Lecture Notes in Computer Science, 4304, 1259-1264, Springer, ISSN 0302-9743.