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Volume 39 Issue 1
Jan.  2021
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Article Navigation >  DIGITAL TECHNOLOGY & APPLICATION  > 2021  >  39(1): 64-66
LU Wen-ling. Handwritten Character Recognition Based on BP Neural Network[J]. DIGITAL TECHNOLOGY & APPLICATION, 2021, 39(1): 64-66. doi: 10.19695/j.cnki.cn12-1369.2021.01.20
Citation: LU Wen-ling. Handwritten Character Recognition Based on BP Neural Network[J]. DIGITAL TECHNOLOGY & APPLICATION, 2021, 39(1): 64-66. doi: 10.19695/j.cnki.cn12-1369.2021.01.20
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Handwritten Character Recognition Based on BP Neural Network

doi: 10.19695/j.cnki.cn12-1369.2021.01.20

  • Nanjing Forestry University, Nanjing Jiangsu 210037

  • Received Date: 2020-11-28
  • Rev Recd Date: 2021-01-17
    • Available Online: 2021-09-23
  • Publish Date: 2021-01-25
    Abstract
  • The article compares twoneural network models trained using TensorFlow on MNIST datasets, and optimizes parameters such as the number of neurons, activation function, learning rate, and optimizer. The results show that the simple neural network model can achieve high performance on handwriting recognition under sufficient training data sets, and the additional technical optimization greatly enhances the generalization ability and robustness of the model.

     

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    • References(4)
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    李丹.基于 BP 神经网络的多样本手写体字符识别[J].软件,2016,37(7):103-108.
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    许宜申,顾济华,陶智,等.基于改进BP神经网络的手写字符识别[J].通信技术,2011,44(5):106-109

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    丁蒙,戴曙光,于恒.卷积神经网络在手写字符识别中的应用[J].软件导刊,2020,19(1):275-279.
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    黎育权.基于集成学习改进的卷积神经网络的手写字符识别[J].电子技术与软件工程,2018(9):167.
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