DIGITAL IMAGE PROCESSING: BINARIZATION

One of the most important and necessary steps
in the process of document analysis and recognition is the
binarization, which allows extracting the foreground from
the background. Several binarization techniques have been
proposed in the literature, but none of themwas reliable for all
image types. This makes the selection of one method to apply
in a given application very difficult. Thus, performance evaluation
of binarization algorithms becomes therefore vital.
In this paper, we are interested in the evaluation of binarization
techniques for the purpose of retrieving words from
the images of degraded Arabic documents. A new evaluation
methodology is proposed. The proposed evaluation
methodology is based on the comparison of the visual features
extracted from the binarized document images with
ground truth features instead of comparing images between
themselves. The most appropriate thresholding method for
each image is the one for which the visual features of the
identified words in the image are “closer” to the features
of the reference words. The proposed technique was used
here to assess the performances of eleven algorithms based
on different approaches on a collection of real and synthetic
images.

This article discusses the quality assessment of binary images. The customary, ground truth based methodology, used in the literature is shown to be problematic due to its subjective nature. Several previously suggested alternatives are surveyed and are also found to be inadequate in certain scenarios. A new approach, quantifying the adherence of a binarization to its document image is proposed and tested using six different measures of accuracy. The measures are evaluated experimentally based on datasets from DIBCO and H-DIBCO competitions, with respect to different kinds of binarization degradations.

In this chapter, a binarization technique specifically designed for historical document images is presented. Existing binarization techniques focus either on finding an appropriate global threshold or adapting a local threshold for each area in order to remove smear, strains, uneven illumination etc. Here, a hybrid approach is presented that first applies a global thresholding technique and, then, identifies the image areas that are more likely to still contain noise. Each of these areas is re-processed separately to achieve better quality of binarization. Evaluation results are presented that compare our technique with existing ones and indicate that the proposed approach is effective, combining the advantages of global and local thresholding. Finally, future directions of our research are mentioned.

Chan-Vese is an important and well-established segmentation method. However, it tends to be challenging to implement, including issues such as initialization problems and establishing the values of several free parameters. The paper presents a detailed analysis of Chan-Vese framework. It establishes a relation between the Otsu binarization method and the fidelity terms of Chan-Vese energy functional, allowing for intelligent initialization of the scheme. An alternative, fast, and parameter-free morphological segmentation technique is also suggested. Our experiments indicate the soundness of the proposed algorithm.

In this paper, we present a novel approach of document image acquisition with reduced number of measurements, requiring lesser number of sensing elements. Reduction in the number of sensors also directly implies faster acquisition. Our approach is built around the compressed sensing paradigm, in which signals which are sparse can be captured and reconstructed with considerably lesser number of measurements than required by Nyquist sensing. Signal reconstruction algorithms used in compressed sensing are computationally intensive. A brief survey of some of the popular reconstruction algorithms is also given in the paper. In the algorithm proposed in this work, the reconstruction step associated with recovering the binary image from the compressed sensing measurements is pruned to take advantage of the inherent requirement of the binarization of the acquired image. Binarization of captured documents, which is an essential step for post processing is built into the proposed scheme.