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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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.
References
Plotz, T., Fink, G.A.: Markov models for offline handwriting recognition: a survey. Int. J. Doc. Anal. Rec. 12, 269–298 (2009)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 70, 257–286 (1989)
Marti, U., Bunke, H.: Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition systems. In: Hidden Markov Models: Applications in Computer Vision, pp. 65–90. World Scientic Publishing Co., Inc. (2002)
Chen, J., Huaigu Cao, R., Natarajan, P.: Gabor features for offline Arabic handwriting recognition. In: Proceedings of International Workshop on Document Analysis and System, IAPR, vol. 9, pp. 53–58 (2010)
Cheng-Lin Liu, M.K., Fujisawa., H.: Gabor feature extraction for character recognition: comparison with gradient feature. In: International Conference Document Analysis and Recognition, vol. 8, pp. 121–125. IEEE (2005)
Ahmed, N., Natarajan, T., Rao, K.: Discrete cosine transfom. IEEE Trans. Comput. 23, 90–93 (1974)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings CVPR (2005)
Felzenszwalb, P.F., Grishick, R.B., McAllister, D., Ramanan, D.: Object detection with discriminatively trained part based models. In: PAMI, vol. 32, pp. 1627–1645. IEEE (2009)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: IEEE International Conference on Computer Vision (2007)
Ojala, T., M.P., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings ICPR, vol. 1, pp. 582–585 (1994)
Llorens, D.: The UJIpenchars database: a pen-based database of isolated handwritten characters. In: Proceedings of International Conference on Language Resource and Evaluation, vol. 6, pp. 2647–2651. LREC (2008)
Grother, P.J.: NIST special database 19 handprinted forms and characters database, National Institute of Standards and Technology (1995)
Young, S., Odell, J., Ollason, D., Valtchev, V., Woodland, P.: The HTK Book: Hidden Markov Models Toolkit V2.1. Wiley, Hoboken (1997)
LeCun, Y., Bottou, L., Y.B., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, vol. 86, pp. 2278–2324. IEEE (1998)
Moustafa, R.: Parallel coordinate and parallel coordinate density plots. In: Interdisciplinary Reviews: Computational Statistics, vol. 3(2), pp. 134–148. Wiley (2011)
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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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