Abstract
Based on neural network with favorable adaptability to handwritten Chinese character multi-features, in this paper a new method is proposed, using existing multi-features as inputs to structure multi neural network recognition subsystems and these subsystems are integrated with parallel connection mode. The integrated system has the lowest false recognition rate. When using traditional von Neumann architecture computer to implement this system, the system response time is longer as a result of serial computation. This paper introduces a kind of parallel computation method of using pc cluster to implement multi subsystems. It can reduce effectively recognition system’s response time.
This paper research is supported from national ministry of education excellent backbone teacher assistance plan.
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References
Weideman, W.E., Manry, M.T., Yau, H.C.: A Comparison of a Nearest Neighbor Classifier and a Neural Network for Numeric Handprint Character Recognition. In: International Joint Conference on Neural Networks, Washington D.C., vol. , pp. 117-120 (1989)
Guyon, I.: Applications of Neural Networks to Character Recognition. International Journal of Pattern Recognition and Artificial Intelligence 5, 353–382 (1991)
Kim, H.J., Kim, K.H., Lee, J.K.: Online Recognition of Handwritten Chinese Characters Based on Hidden Markov Models. Pattern Recognition 30, 1489–1500 (1997)
Fogelman, S., Viennet, E., Lamy, B.: Multi-Modular Neural Network Architectures: Applications in Optical Character and Human Face Recognition. Pattern Recognition and Artificial Intelligence 7, 521–555 (1993)
Yang, H.M., Jiang, H.L.: A Method of Integrating Multiple Handwritten Recognition System Based on ANN. ACTA ARMAMENTARII 19, 339–343 (1998)
Hill, J.M.D, McColl, B., Stefanescu, D.C.: The BSP Programming Library. Technical Report PRG-TR-29-97, Oxford University Computing Laboratory (1997)
Hill, J.M.D., Skillicorn, D.B.: Lessons Learned from Implementing BSP. Journal of Future Generation Computer Systems 13, 327–335 (1998)
Yang, H.M.: Combinations of Neural Networks and its Application. Journal of Changchun Inst. Opt. & Fine Mech. 21, 39–43 (1998)
Huang, T.S., Suen, C.Y.: Combination of Multiple Experts for the Recognition of Unconstrained Handwritten Numerals. Pattern Analysis and Machine Intelligence 17, 90–94 (1995)
Suen, C.Y., Nadal, C., Legault, R., Mai, T.A., Lam, L.: Computer Recognition of Unconstrained Handwritten Numerals. Proceedings of the IEEE 80, 1162–1181 (1992)
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Li, Y., Yang, H., Xu, J., He, W., Fan, J. (2007). Chinese Character Recognition Method Based on Multi-features and Parallel Neural Network Computation. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_112
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DOI: https://doi.org/10.1007/978-3-540-74171-8_112
Publisher Name: Springer, Berlin, Heidelberg
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