On-Line Handwriting Recognition Based on Hopfield Neural Network

Jing Ya Zhang; Li Yang; Rong Zhao; Long Hua Yang

doi:10.4028/www.scientific.net/AMM.610.265

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 610On-Line Handwriting Recognition Based on Hopfield...

On-Line Handwriting Recognition Based on Hopfield Neural Network

Abstract:

In this paper, Discrete Hopfield Neural Network (DHNN) is adopted to realize handwritten characters recognition. First, learning samples are preprocessed including binarization, normalization and interpolation. Then pixel features are extracted and used to establish DHNN. The handwritten test samples and noise corrupted samples are finally inputted into the network to verify its recognition performance. Simulation results reveal that DHNN has good fault tolerance and disturbance rejection performance. In addition, the recognition system is realized with MATLAB neural network toolbox and GUI, which verifies the feasibility of the algorithm.

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Periodical:

Applied Mechanics and Materials (Volume 610)

Pages:

265-269

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.610.265

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Online since:

August 2014

Authors:

Jing Ya Zhang, Li Yang*, Rong Zhao, Long Hua Yang

Keywords:

Handwriting Recognition, Hopfield Neural Network, Pattern Recognition

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* - Corresponding Author

References

[1] Ming Chang, Shi Han, Dongmei Zhang, A Unified Framwork for Recognizing Handwritten Chemical Expressions, 2009, 10th International Conference on Document Analysis and Recognition, pp: 1345-1349.

DOI: 10.1109/icdar.2009.64

Google Scholar

[2] Jiang Han, Liu ChangSong, Li ShiYang. An On-line Handwritten Arithmetic Expression Recognition System [J]. Journal of Electronic & Information Technology, 2010, 5, Vol. 32, No. 5: 1126-1230.

Google Scholar

[3] An Bo, Yang ShuYing, Wang LiQun. Handwritten character recognition based on improved Hopfield neural network [J]. Journal of Tianjin University of Technology, 2009, Vol. 25, No. 4: 49-52.

Google Scholar

[4] Vandana Roy and Sriganesh, Anand S and Raghunath R. Sharma, A Framework for Adaption of the Active-DTW classifier for Online Handwritten Character Recognition, 2009, 10th International Conference on Document Analysis and Recognition, pp: 401-405.

DOI: 10.1109/icdar.2009.195

Google Scholar

[5] Yin Xuan, Wang Sheng. Research on Letter Distinguish Based on Discrete Hopfield Neural Net [J]. Computer & Digital Engineering. 2011(01), Vol. 39, No. 1: 35-38.

Google Scholar

[6] Lv Fen, Zhao ShengMei. The Noise Character Recognition Based on Hopfield Neural Network [J]. Computer & Information Technology, 2005, Vol12, No. 26: 20-21.

Google Scholar

[7] Cong Shuang, Lu TingTing. Design of three artificial neural networks used on English character recognition [J]. Chinese Journal of Scientific Instrument, 2006, Vol. 27, No. 6: 2242-2244.

Google Scholar

[8] Wang QiRui, Zhu WanLing. A Method for Identifying Noised Words Based on MATLAB [J]. Computer Simulation, 2002, 7, Vol. 19, No. 4: 91-93.

Google Scholar

[9] Ma PengFei, Yang Fan, Zhang ChunRui. Analysis of Discrete Hopfield Neural Network[J]. Mathematics In Practice And Theory, 2010, Vol. 40, No. 16: 203-207.

Google Scholar