Abstract
Handwritten Digit Recognition (HDR) has become one of the challenging areas of research in the field of document image processing during the last few decades. In this paper, inspired by the success of the very deep state-of-the-art VGGNet, we proposed VGG_No for HDR. VGG_No is fast and reliable, which improved the classification performance effectively. Besides, this model has also reduced the overall complexity of VGGNet. VGG_No constructed by thirteen convolutional layers, two max-pooling layers, and three fully connected layers. A Cross-Validation analysis has been performed using the 10-Fold Cross-Validation strategy, and 10-Fold classification accuracies of 99.57% and 99.69% have been obtained for ADBase database and MNIST database, respectively. The classification performance of VGG_No is superior to existing techniques using multi-classifiers since it has achieved better results using very simple and homogeneous architecture.
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Acknowledgments
This research was supported in part by Science & Technology Pillar Program of Hubei Province under Grant (#2014BAA146), Nature Science Foundation of Hubei Province under Grant (#2015CFA059), Science and Technology Open Cooperation Program of Henan Province under Grant (#152106000048).
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Almodfer, R., Xiong, S., Mudhsh, M., Duan, P. (2017). Very Deep Neural Networks for Hindi/Arabic Offline Handwritten Digit Recognition. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_47
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