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Hand-printed character recognition is an important application in our life. A method called the feature-to-feature adhesion method is developed to achieve the task. There are two geometrical relations constructed in an earlier method. This work rewrites geometrical relations and constructs them into the Hopfield model to improve the matching result. We also provided some new applications which can be solved by our improved method.
Rocznik
Tom
Strony
59--76
Opis fizyczny
Bibliogr. 14 poz., rys.
Twórcy
autor
autor
autor
- Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, cyliou@csie.ntu.edu.tw
Bibliografia
- [1] Rocha, J., Pavlidis, T.: A Shape Analysis Model with Applications to A Character Recognition System. IEEE Trans. Pattern Anal. Mach. Intell., 16(4), pp. 393–404, 1994.
- [2] Rocha, J., Pavlidis, T.: Character Recognition Without Segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 17(9), pp. 903–909, 1995.
- [3] Lu, S. W., Ren, Y., Suen, C. Y.: Hierarchical Attributed Graph Representation And Recognition of Handwritten Chinese Characters. Pattern Recognition, 24(7), pp. 617–632, 1991.
- [4] Liou, C. Y., Yang, H. C.: Selective Feature-to-Feature Adhesion for Recognition of Cursive Handprinted Characters. IEEE Trans. Pattern Anal. Mach. Intell., 21(2), pp. 184–191, 1999.
- [5] Hopfield, J., Tank, D.: Neural Computation of Decisions in Optimization Problems. Biological cybernetics, 52(3), pp. 141–152, 1985.
- [6] Liou, C. Y., Yang, H. C.: Self-Organization of High-Order Receptive Fields in Recognition of Handprinted Characters. In: Neural Information Processing, 1999. Proceedings. ICONIP’99. 6th International Conference on, volume 3, pp. 1161–1166. IEEE, 1999.
- [7] Moscona, A., Moscona, H.: The Dissociation and Aggregation of Cells from Organ Rudiments of The Early Chick Embryo. Journal of anatomy, 86(3), p. 287, 1952.
- [8] Townes, P. L., Holtfreter, J.: Directed Movements And Selective Adhesion of Embryonic Amphibian Cells. Journal of experimental zoology, 128(1), pp. 53–120, 1955.
- [9] Nasrabadi, N. M., Li, W., Choo, C. Y.: Object Recognition by a Hopfield Neural Network. In: ICCV, pp. 325–328. 1990.
- [10] Suganthan, P. N., Teoh, E. K., Mital, D. P.: Pattern Recognition by Homomorphic Graph Matching Using Hopfield Neural Networks. Image Vision Comput., 13(1), pp. 45–60, 1995.
- [11] Szu, H.: Fast TSP Algorithm Based on Binary Neuron Output and Analog Neuron Input Using The Zero-Diagonal Interconnect Matrix and Necessary and Sufficient Constraints of the Permutation Matrix. IEEE Trans. Int’l Conf Neural Networks, 2, pp. 259–266, 1988.
- [12] Aiyer, S., Niranjan, M., Fallside, F.: A Theoretical Investigation Into the Performance of the Hopfield Model. Neural Networks, IEEE Transactions on, 1(2), pp. 204–215, 1990.
- [13] Rummelhart, D. E., Hinton, G., McClelland, J. L.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1986.
- [14] Liou, C. Y., Yu, W. J.: Ambiguous Binary Representation in Multilayer Neural Networks. In: Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 1, pp. 379–384. IEEE, 1995.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS3-0025-0133