CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Deep Networks for Degraded Document Image Binarization through Pyramid Reconstruction
Gaofeng Meng; Kun Yuan; Ying Wu; Shiming Xiang; Chunhong Pan
Conference NameInternational Conference on Document Analysis and Recognition (ICDAR)
Conference DateNovember 13-15, 2017
Conference PlaceKyoto, Japan
Binarization of document images is an important
processing step for document images analysis and recognition.
However, this problem is quite challenging in some cases because
of the quality degradation of document images, such as
varying illumination, complicated backgrounds, image noises
due to ink spots, water stains or document creases. In this
paper, we propose a framework based on deep convolutional
neural-network (DCNN) for adaptive binarization of degraded
document images. The basic idea of our method is to decompose
a degraded document image into a spatial pyramid structure
by using DCNN, with each layer at different scale. Then the
foreground image is sequentially reconstructed from these layers
in a coarse-to-fine manner by using deconvolutional network.
Such kind of decomposition is quite beneficial, since multiresolution
supervision information can be directly introduced into
network learning.We also define several loss functions about label
consistency and foregrounds smoothing to further regularize the
training of the network. Experimental results demonstrate the
effectiveness of the proposed method.
Document Type会议论文
Recommended Citation
GB/T 7714
Gaofeng Meng,Kun Yuan,Ying Wu,et al. Deep Networks for Degraded Document Image Binarization through Pyramid Reconstruction[C],2017:727-732.
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