Abstract
Hidden Markov Models constitute an established approach often employed for offline handwritten character recognition in digitized documents. The current work aims at evaluating a number of procedures frequently used to define features in the character recognition literature, within a common Hidden Markov Model framework. By separating model and feature structure, this should give a more clear indication of the relative advantage of different families of visual features used for character classification. The effects of model topologies and data normalization are also studied over two different handwritten datasets. The Hidden Markov Model framework is then used to generate images of handwritten characters, to give an accessible visual illustration of the power of different features.
This project is a part of q2b, From quill to bytes, an initiative sponsored by the “Swedish Research Council” Vetenskapsrådet (D.Nr 2012-5743) and Riksbankens Jubileumsfond (R.Nr NHS14-2068:1) and Uppsala university.
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Notes
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The authors would like to thank Alicia Fornes and Computer Vision Center at Universitat Autonoma de Barcelona for their help in extracting the character images from the NIST data-set.
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Ayyalasomayajula, K.R., Nettelblad, C., Brun, A. (2016). Feature Evaluation for Handwritten Character Recognition with Regressive and Generative Hidden Markov Models. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_26
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DOI: https://doi.org/10.1007/978-3-319-50835-1_26
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