1 research outputs found
DropRegion Training of Inception Font Network for High-Performance Chinese Font Recognition
Chinese font recognition (CFR) has gained significant attention in recent
years. However, due to the sparsity of labeled font samples and the structural
complexity of Chinese characters, CFR is still a challenging task. In this
paper, a DropRegion method is proposed to generate a large number of stochastic
variant font samples whose local regions are selectively disrupted and an
inception font network (IFN) with two additional convolutional neural network
(CNN) structure elements, i.e., a cascaded cross-channel parametric pooling
(CCCP) and global average pooling, is designed. Because the distribution of
strokes in a font image is non-stationary, an elastic meshing technique that
adaptively constructs a set of local regions with equalized information is
developed. Thus, DropRegion is seamlessly embedded in the IFN, which enables
end-to-end training; the proposed DropRegion-IFN can be used for high
performance CFR. Experimental results have confirmed the effectiveness of our
new approach for CFR.Comment: 15 pages, 7 figure