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

Image Enhancement with Statistical Estimation

Contrast enhancement is an important area of research for the image analysis. Over the decade, the researcher worked on this domain to develop an efficient and adequate algorithm. The proposed method will enhance the contrast of image using Binarization method with the help of Maximum Likelihood Estimation (MLE). The paper aims to enhance the image contrast of bimodal and multi-modal images. The proposed methodology use to collect mathematical information retrieves from the image. In this paper, we are using binarization method that generates the desired histogram by separating image nodes. It generates the enhanced image using histogram specification with binarization method. The proposed method has showed an improvement in the image contrast enhancement compare with the other image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print

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

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

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

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

Binarization Technique on Historical Documents using Edge Width Detection

Document images often suffer from different types of degradation that renders the document image binarization is a challenging task. Document image binarization is of great significance in the document image analysis and recognition process because it affects additional steps of the recognition development. The Comparison of image gradient and image contrast is estimated by the local maximum and minimum which has high quality. So, it is more tolerant to the rough lighting and other types of document degradation such as low contrast images and partially visible images. The distinction between the foreground text and the background text of different document images is a difficult task. This paper presents a new document image binarization technique that focus on these issues using adaptive image contrast. The grouping of the local image contrast and the local image slope is the adaptive image contrast so as to tolerate the text and surroundings distinction caused by dissimilar types of text degradations. In image binarization technique, the construction of adaptive contrast map is done for an input degraded document image which is then adaptively binarized and combined with Canny’s edge detector to recognize the text stroke edge pixels. The document text is advance segmented by means of a local threshold. We try to apply the self-training adaptive binarization approach on existing binarization methods, which improves not only the performance of existing binarization methods, but also the, toughness on different kinds of degraded document images. DOI: 10.17762/ijritcc2321-8169.15066