期刊
PATTERN RECOGNITION
卷 91, 期 -, 页码 379-390出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2019.01.025
关键词
Document enhancement and binarization; Convolutional neural networks; Iterative deep learning; Recurrent refinement
资金
- Dutch Organization for Scientific Research NWO Digging into data grant 'Global Currents' [640.006.015]
This paper presents a novel iterative deep learning framework and applies it to document enhancement and binarization. Unlike the traditional methods that predict the binary label of each pixel on the input image, we train the neural network to learn the degradations in document images and produce uniform images of the degraded input images, which in turn allows the network to refine the output iteratively. Two different iterative methods have been studied in this paper: recurrent refinement (RR) that uses the same trained neural network in each iteration for document enhancement and stacked refinement (SR) that uses a stack of different neural networks for iterative output refinement. Given the learned nature of the uniform and enhanced image, the binarization map can be easily obtained through use of a global or local threshold. The experimental results on several public benchmark data sets show that our proposed method provides a new, clean version of the degraded image, one that is suitable for visualization and which shows promising results for binarization using Otsu's global threshold, based on enhanced images learned iteratively by the neural network. (C) 2019 Elsevier Ltd. All rights reserved.
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