43 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
Automatic Document Image Binarization using Bayesian Optimization
Document image binarization is often a challenging task due to various forms
of degradation. Although there exist several binarization techniques in
literature, the binarized image is typically sensitive to control parameter
settings of the employed technique. This paper presents an automatic document
image binarization algorithm to segment the text from heavily degraded document
images. The proposed technique uses a two band-pass filtering approach for
background noise removal, and Bayesian optimization for automatic
hyperparameter selection for optimal results. The effectiveness of the proposed
binarization technique is empirically demonstrated on the Document Image
Binarization Competition (DIBCO) and the Handwritten Document Image
Binarization Competition (H-DIBCO) datasets
A Multiple-Expert Binarization Framework for Multispectral Images
In this work, a multiple-expert binarization framework for multispectral
images is proposed. The framework is based on a constrained subspace selection
limited to the spectral bands combined with state-of-the-art gray-level
binarization methods. The framework uses a binarization wrapper to enhance the
performance of the gray-level binarization. Nonlinear preprocessing of the
individual spectral bands is used to enhance the textual information. An
evolutionary optimizer is considered to obtain the optimal and some suboptimal
3-band subspaces from which an ensemble of experts is then formed. The
framework is applied to a ground truth multispectral dataset with promising
results. In addition, a generalization to the cross-validation approach is
developed that not only evaluates generalizability of the framework, it also
provides a practical instance of the selected experts that could be then
applied to unseen inputs despite the small size of the given ground truth
dataset.Comment: 12 pages, 8 figures, 6 tables. Presented at ICDAR'1
Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques
One of the most important steps of document image processing is binarization.
The computational requirements of locally adaptive binarization techniques make
them unsuitable for devices with limited computing facilities. In this paper,
we have presented a computationally efficient implementation of convolution
based locally adaptive binarization techniques keeping the performance
comparable to the original implementation. The computational complexity has
been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the
image size. Experiments over benchmark datasets show that the computation time
has been reduced by 5 to 15 times depending on the window size while memory
consumption remains the same with respect to the state-of-the-art algorithmic
implementation