24 research outputs found
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
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
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
A selectional auto-encoder approach for document image binarization
Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of document analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types.This work was partially supported by the Social Sciences and Humanities Research Council of Canada, the Spanish Ministerio de Ciencia, Innovación y Universidades through Juan de la Cierva - Formación grant (Ref. FJCI-2016-27873), and the Universidad de Alicante through grant GRE-16-04
Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks
The efficient segmentation of foreground text information from the background
in degraded color document images is a hot research topic. Due to the imperfect
preservation of ancient documents over a long period of time, various types of
degradation, including staining, yellowing, and ink seepage, have seriously
affected the results of image binarization. In this paper, a three-stage method
is proposed for image enhancement and binarization of degraded color document
images by using discrete wavelet transform (DWT) and generative adversarial
network (GAN). In Stage-1, we use DWT and retain the LL subband images to
achieve the image enhancement. In Stage-2, the original input image is split
into four (Red, Green, Blue and Gray) single-channel images, each of which
trains the independent adversarial networks. The trained adversarial network
models are used to extract the color foreground information from the images. In
Stage-3, in order to combine global and local features, the output image from
Stage-2 and the original input image are used to train the independent
adversarial networks for document binarization. The experimental results
demonstrate that our proposed method outperforms many classical and
state-of-the-art (SOTA) methods on the Document Image Binarization Contest
(DIBCO) dataset. We release our implementation code at
https://github.com/abcpp12383/ThreeStageBinarization