824 research outputs found
Persian Heritage Image Binarization Competition (PHIBC 2012)
The first competition on the binarization of historical Persian documents and
manuscripts (PHIBC 2012) has been organized in conjunction with the first
Iranian conference on pattern recognition and image analysis (PRIA 2013). The
main objective of PHIBC 2012 is to evaluate performance of the binarization
methodologies, when applied on the Persian heritage images. This paper provides
a report on the methodology and performance of the three submitted algorithms
based on evaluation measures has been used.Comment: 4 pages, 2 figures, conferenc
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
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
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
Enhancement of Image Resolution by Binarization
Image segmentation is one of the principal approaches of image processing.
The choice of the most appropriate Binarization algorithm for each case proved
to be a very interesting procedure itself. In this paper, we have done the
comparison study between the various algorithms based on Binarization
algorithms and propose a methodologies for the validation of Binarization
algorithms. In this work we have developed two novel algorithms to determine
threshold values for the pixels value of the gray scale image. The performance
estimation of the algorithm utilizes test images with, the evaluation metrics
for Binarization of textual and synthetic images. We have achieved better
resolution of the image by using the Binarization method of optimum
thresholding techniques.Comment: 5 pages, 8 figure
Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images
In recent years, a large number of binarization methods have been developed,
with varying performance generalization and strength against different
benchmarks. In this work, to leverage on these methods, an ensemble of experts
(EoE) framework is introduced, to efficiently combine the outputs of various
methods. The proposed framework offers a new selection process of the
binarization methods, which are actually the experts in the ensemble, by
introducing three concepts: confidentness, endorsement and schools of experts.
The framework, which is highly objective, is built based on two general
principles: (i) consolidation of saturated opinions and (ii) identification of
schools of experts. After building the endorsement graph of the ensemble for an
input document image based on the confidentness of the experts, the saturated
opinions are consolidated, and then the schools of experts are identified by
thresholding the consolidated endorsement graph. A variation of the framework,
in which no selection is made, is also introduced that combines the outputs of
all experts using endorsement-dependent weights. The EoE framework is evaluated
on the set of participating methods in the H-DIBCO'12 contest and also on an
ensemble generated from various instances of grid-based Sauvola method with
promising performance.Comment: 6-page version, Accepted to be presented in ICDAR'1
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.Comment: 25 pages, submitted version, To appear in International Journal on
Document Analysis and Recognition, On line version available at
http://www.springerlink.com/content/k2813176280456k3
COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation
The absence of large scale datasets with pixel-level supervisions is a
significant obstacle for the training of deep convolutional networks for scene
text segmentation. For this reason, synthetic data generation is normally
employed to enlarge the training dataset. Nonetheless, synthetic data cannot
reproduce the complexity and variability of natural images. In this paper, a
weakly supervised learning approach is used to reduce the shift between
training on real and synthetic data. Pixel-level supervisions for a text
detection dataset (i.e. where only bounding-box annotations are available) are
generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which
provides pixel-level supervisions for the COCO-Text dataset, is created and
released. The generated annotations are used to train a deep convolutional
neural network for semantic segmentation. Experiments show that the proposed
dataset can be used instead of synthetic data, allowing us to use only a
fraction of the training samples and significantly improving the performances
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