57 research outputs found
DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning
This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
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 the uniform
images of the degraded input images, which allows the network to refine the
output iteratively. Two different iterative methods have been studied in this
paper: recurrent refinement (RR) which uses the same trained neural network in
each iteration for document enhancement and stacked refinement (SR) which uses
a stack of different neural networks for iterative output refinement. Given the
learned uniform and enhanced image, the binarization map can be easy to obtain
by a global or local threshold. The experimental results on several public
benchmark data sets show that our proposed methods provide a new clean version
of the degraded image which is suitable for visualization and promising results
of binarization using the global Otsu's threshold based on the enhanced images
learned iteratively by the neural network.Comment: Accepted by Pattern Recognitio
CT-Net:Cascade T-shape deep fusion networks for document binarization
Document binarization is a key step in most document analysis tasks. However, historical-document images usually suffer from various degradations, making this a very challenging processing stage. The performance of document image binarization has improved dramatically in recent years by the use of Convolutional Neural Networks (CNNs). In this paper, a dual-task, T-shaped neural network is proposed that has the main task of binarization and an auxiliary task of image enhancement. The neural network for enhancement learns the degradations in document images and the specific CNN-kernel features can be adapted towards the binarization task in the training process. In addition, the enhancement image can be considered as an improved version of the input image, which can be fed into the network for fine-tuning, making it possible to design a chained-cascade network (CT-Net). Experimental results on document binarization competition datasets (DIBCO datasets) and MCS dataset show that our proposed method outperforms competing state-of-the-art methods in most cases
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising
Degraded images commonly exist in the general sources of character images,
leading to unsatisfactory character recognition results. Existing methods have
dedicated efforts to restoring degraded character images. However, the
denoising results obtained by these methods do not appear to improve character
recognition performance. This is mainly because current methods only focus on
pixel-level information and ignore critical features of a character, such as
its glyph, resulting in character-glyph damage during the denoising process. In
this paper, we introduce a novel generic framework based on glyph fusion and
attention mechanisms, i.e., CharFormer, for precisely recovering character
images without changing their inherent glyphs. Unlike existing frameworks,
CharFormer introduces a parallel target task for capturing additional
information and injecting it into the image denoising backbone, which will
maintain the consistency of character glyphs during character image denoising.
Moreover, we utilize attention-based networks for global-local feature
interaction, which will help to deal with blind denoising and enhance denoising
performance. We compare CharFormer with state-of-the-art methods on multiple
datasets. The experimental results show the superiority of CharFormer
quantitatively and qualitatively.Comment: Accepted by ACM MM 202
Uniqueness of Bilevel Image Degradations
Two major degradations, edge displacement and corner erosion, change the appearance of bilevel images. The displacement of an edge determines stroke width, and the erosion ofa corner affects crispness. These degradations are functions of the system parameters: the point spread function (PSF) width and functional form, and the binarization threshold. Changing each of these parameters will affect an image differently. A given amount of edge displacement or amount of erosion of black or white corners can be caused by several combinations of the PSF width and the binarization threshold. Any pair of these degradations are unique to a single PSF width and binarization threshold for a given PSF function. Knowledge of all three degradation amounts provides information that will enable us to determine the PSF functional form from the bilevel image. The effect of each degradation on characters will be shown. Also, the uniqueness of the degradation triple {dw\u3e db, δc} and the effect of selecting an incorrect PSF functional form will be shown, first with relation to PSF width and binarization threshold estimate, then for how this is visible in sample characters
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