26 research outputs found
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
Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing
Computation of document image quality metrics often depends upon the
availability of a ground truth image corresponding to the document. This limits
the applicability of quality metrics in applications such as hyperparameter
optimization of image processing algorithms that operate on-the-fly on unseen
documents. This work proposes the use of surrogate models to learn the behavior
of a given document quality metric on existing datasets where ground truth
images are available. The trained surrogate model can later be used to predict
the metric value on previously unseen document images without requiring access
to ground truth images. The surrogate model is empirically evaluated on the
Document Image Binarization Competition (DIBCO) and the Handwritten Document
Image Binarization Competition (H-DIBCO) datasets
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
Deformable Part Models for Automatically Georeferencing Historical Map Images
Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Using an evaluation set of 20 historical maps from states and regions of the U.S., the method reduces average alignment RMSE by 12%
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
U-Net-bin: hacking the document image binarization contest
Image binarization is still a challenging task in a variety of applications. In particular, Document Image Binarization Contest (DIBCO) is organized regularly to track the state-of-the-art techniques for the historical document binarization. In this work we present a binarization method that was ranked first in the DIBCO`17 contest. It is a convolutional neural network (CNN) based method which uses U-Net architecture, originally designed for biomedical image segmentation. We describe our approach to training data preparation and contest ground truth examination and provide multiple insights on its construction (so called hacking). It led to more accurate historical document binarization problem statement with respect to the challenges one could face in the open access datasets. A docker container with the final network along with all the supplementary data we used in the training process has been published on Github.The work was partially funded by Russian Foundation for Basic Research (projects 17-29-07092 and 17-29-07093)
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
An Efficient Phase-Based Binarization Method for Degraded Historical Documents
Document image binarization is the first essential step in digitalizing images and is considered an essential technique in both document image analysis applications and optical character recognition operations, the binarization process is used to obtain a binary image from the original image, binary image is the proper presentation for image segmentation, recognition, and restoration as underlined by several studies which assure that the next step of document image analysis applications depends on the binarization result. However, old and historical document images mainly suffering from several types of degradations, such as bleeding through the blur, uneven illumination and other types of degradations which makes the binarization process a difficult task. Therefore, extracting of foreground from a degraded background relies on the degradation, furthermore it also depends on the type of used paper and document age. Developed binarization methods are necessary to decrease the impact of the degradation in document background. To resolve this difficulty, this paper proposes an effective, enhanced binarization technique for degraded and historical document images. The proposed method is based on enhancing an existing binarization method by modifying parameters and adding a post-processing stage, thus improving the resulting binary images. This proposed technique is also robust, as there is no need for parameter tuning. After using document image binarization Contest (DIBCO) datasets to evaluate this proposed technique, our findings show that the proposed method efficiency is promising, producing better results than those obtained by some of the winners in the DIBCO
Isolated Character Forms from Dated Syriac Manuscripts
This paper describes a set of hand-isolated character samples selected from securely dated manuscripts written in Syriac between 300 and 1300 C.E., which are being made available for research purposes. The collection can be used for a number of applications, including ground truth for character segmentation and form analysis for paleographical dating. Several applications based upon convolutional neural networks demonstrate the possibilities of the data set