HIERARCHICAL CONSTRAINED AUTOMATIC LEARNING NETWORK FOR CHARACTER RECOGNITION

摘要

Highly accurate, reliable optical character recognition is afforded by a layered network having several layers of constrained feature detection wherein each layer of constrained feature detection includes a plurality of constrained feature maps and a corresponding plurality of feature reduction maps. Each feature reduction map is connected to only one constrained feature map in the same layer for undersampling that constrained feature map. Units in each constrained feature map of the first constrained feature detection layer respond as a function of a corresponding kernel and of different portions of the pixel image of the character captured in a receptive field associated with the unit. Units in each feature map of the second constrained feature detection layer respond as a function of a corresponding kernel and of different portions of an individual feature reduction map or a combination of several feature reduction maps in the first constrained feature detection layer as captured in a receptive field of the unit. The feature reduction maps of the second constrained feature detection layer are fully connected to each unit in the final character classification layer. Kernels are automatically learned by constrained back propagation during network initialization or training.