ILBPSDNet: Based on improved local binary pattern shallow deep convolutional neural network for character recognition
Shih‐Hsiung Lee, Wei‐Fu Yu, Chu‐Sing Yang
Abstract
Abstract This paper proposes an architecture based on the improved local binary pattern (LBP) shallow deep convolution neural network, which integrates hand‐crafted feature pre‐processing and the advantage of character learning in the supervised high‐level function of CNN, in order to enhance its performance. This study introduced the information of scale space into the LBP to reduce the sensitivity to noise, and applied feature maps with two features, the maximum selection feature map (MLBP) and the first selection feature map (FLBP). The former selected the edge with the strongest intensity to reduce the influence of noise points, while the latter measured local binary features through the scale detection of an effective edge. In the network architecture design, according to the differences of input features, networks of different depths were used for learning, and the features learned by the two networks were adopted for classification. The experimental results show that, the ILBPSDNet proposed had certain recognition abilities in many character data sets, and the network parameters and computation were also reduced. Therefore, it has a significant effect in realizing the application of real‐time character recognition. Finally, compared with other latest networks, its network performance could be maintained at a certain level.