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Shape Matching and Hand Written Digit Identification Using Minkowski Distance

K. Senthil Kumar, S. Elavarasan


Hand written digit recognition is challenging problem in real world application. The digits can be identified by its shape. The shape of the digit from the image is taken as the feature. The shape matching can be achieved by the distance between the reference and test shape. The distance is considered as the error between the test and error shape. By using this error, the images are classified into the digits. The normal NN-classifier is used in our system. Our proposed method is applied to MNIST hand written digit database. The algorithm developed using MATLAB and results were obtained.


Digit Recognition, MNIST Data Base.

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Manay, S., Cremers, D., Hong, B. W., Yezzi, A. J., & Soatto, S. (2006). Integral invariants for shape matching. IEEE Transactions on pattern analysis and machine intelligence, 28(10), 1602-1618.

Riba, P., Lladãs, J., & Fornés, A. (2015, August). Handwritten word spotting by inexact matching of grapheme graphs. In Document Analysis and Recognition (ICDAR), 2015 13th International Conference on (pp. 781-785). IEEE.

Dutta, A., Lladós, J., Bunke, H., & Pal, U. (2013, August). A product graph based method for dual subgraph matching applied to symbol spotting. In International Workshop on Graphics Recognition (pp. 11-24). Springer Berlin Heidelberg.

Fischer, A., Baechler, M., Garz, A., Liwicki, M., & Ingold, R. (2014, April). A combined system for text line extraction and handwriting recognition in historical documents. In Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on (pp. 71-75). IEEE.

Cruz, R. M., Cavalcanti, G. D., & Ren, T. I. (2010). Handwritten digit recognition using multiple feature extraction techniques and classifier ensemble. In 17th International Conference on Systems, Signals and Image Processing (pp. 215-218).

Sebastian, T. B., Klein, P. N., & Kimia, B. B. (2004). Recognition of shapes by editing their shock graphs. IEEE Transactions on pattern analysis and machine intelligence, 26(5), 550-571.

Belongie S., Malik J. and Puzicha J., ‘Shape Context: A new descriptor for shaping and object recognition’ IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol 24, pp. 831 – 837, 2002.

Lowe D.G., ‘Object Recognition from Local Scale - Invariant Features’ Proc. Seventh International Conference on Computer Vision, pp. 1150 – 1157, 1999.

Milios E. and Petrakis E., ‘Shape Retrieval Based on Dynamic Programming’ IEEE Transactions on Image Processing, Vol 9 (1), pp. 141 – 147, 2002.

Morton Nadler, ‘Pattern Recognition Engineering’, John Wiley & Sons, 2000.


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