Deep Networks for Degraded Document Image Binarization through Pyramid Reconstruction

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Deep Networks for Degraded Document Image Binarization through Pyramid Reconstruction
	Gaofeng Meng; Kun Yuan; Ying Wu; Shiming Xiang; Chunhong Pan
	2017
会议名称	International Conference on Document Analysis and Recognition (ICDAR)
页码	727-732
会议日期	November 13-15, 2017
会议地点	Kyoto, Japan
摘要	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.
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15339
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
推荐引用方式 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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