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HWNet v2: An Efficient Word Image Representation for Handwritten Documents
[article]
2019
arXiv
pre-print
We present a framework for learning an efficient holistic representation for handwritten word images. The proposed method uses a deep convolutional neural network with traditional classification loss. The major strengths of our work lie in: (i) the efficient usage of synthetic data to pre-train a deep network, (ii) an adapted version of the ResNet-34 architecture with the region of interest pooling (referred to as HWNet v2) which learns discriminative features for variable sized word images,
arXiv:1802.06194v2
fatcat:mr2gvfy775hpzmncvrnprecwb4