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논문 기본 정보

자료유형
학술저널
저자정보
Jie Yang (Wuhan University of Technology) Yadong Sun (Wuhan University of Technology) Liangjun Zhang (Wuhan University of Technology) Qingnian Zhang (Wuhan University of Technology)
저널정보
대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.10 No.2
발행연도
2015.3
수록면
699 - 707 (9page)

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초록· 키워드

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Although digit character recognition has got a significant improvement in recent years, it is still challenging to achieve satisfied result if the data contains an amount of distracting factors. This paper proposes a novel digit character recognition approach using a multi-layer hierarchical model, Hybrid Restricted Boltzmann Machines (HRBMs), which allows the learning architecture to be robust to background distracting factors. The insight behind the proposed model is that useful high-level features appear more frequently than distracting factors during learning, thus the high-level features can be decompose into hybrid hierarchical structures by using only small label information. In order to extract robust and compact features, a stochastic 0-1 layer is employed, which enables the model’s hidden nodes to independently capture the useful character features during training. Experiments on the variations of Mixed National Institute of Standards and Technology (MNIST) dataset show that improvements of the multi-layer hierarchical model can be achieved by the proposed method. Finally, the paper shows the proposed technique which is used in a real-world application, where it is able to identify digit characters under various complex background images.

목차

Abstract
1. Introduction
2. Model Description
3. Experiments and Results
4. Conclusion
References

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