复杂系统管理与控制国家重点实验室知识库

Recognizing scene character is extremely challenging due to various interference factors such as character translation, blur and uneven illumination, etc. Considering that characters are composed of a series of parts and different parts attract diverse attentions when people observe a character, we should assign different importance to each part to recognize scene character. In this paper, we propose a discriminative character representation by aggregating the responses of the spatially embedded salient part detectors. Specifically, we first extract the convolution activations from the pre-trained convolutional neural network (CNN). These convolutional activations are considered as the local descriptors of the character parts. Then we learn a set of part detectors and pick the distinctive convolutional activations which respond to the salient parts. Moreover, to alleviate the effect of character translation, rotation and deformation, etc, we assign a response region for each part detector and search the maximal response in this region.