Handwritten Numbers and English Characters Recognition System

@inproceedings{Li2016HandwrittenNA,
  title={Handwritten Numbers and English Characters Recognition System},
  author={Wei Li and Xiaoxuan He and Chao Tang and Keshou Wu and Xuhui Chen and Shaoyong Yu and Yuliang Lei and Yanan Fang and Yuping Song},
  booktitle={ECC},
  year={2016}
}
In this paper, we design a recognition system of the handwritten numerals and English characters based on BP neural network. In the system, we first make some preprocess to the image. Secondly, we extract the structural and statistical features of the image. Thirdly, we train a model on the data sets via BP neural network. Finally, we can predict the test image using the trained model. The experiments show that the handwritten numbers recognition rate can reach more than 94 % and the… CONTINUE READING

Results and Topics from this paper.

Key Quantitative Results

  • The experiments show that the handwritten numbers recognition rate can reach more than 94 % and the handwritten English characters is 44 % respectively.