Information Density Based Image Binarization for Text Document Containing Graphics
In this work, a new clustering based binarization technique has been proposed. Clustering is done depending on the information density of the input image. Here input image is considered as a set of text, images as foreground and some random noises, marks
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duction
It is very important to maintain the documents and the legacy of the document. To fulfill these purpose document image processing takes a vital role. Document image binarization is usually performed in optical character recognition (OCR) [1,2] and image searching. This involves handwriting recognition, extracting logos and pictures from a graphical image. The main purpose of document image processing [3] is reduction of paper usage, easy access to the documents with lowest storage cost. At this point the most challenging task is to segment the region of interest (ROI) for further analysis. The simplest method for image segmentation [4] is thresholding based binarization which is also an essential technique in enhancement and biomedical image analysis. The output of this process is a binary image [5]. Though researchers work upon document image binarization for several years, the thresholding of compound document images still remains a challenging task due to its sensitivity to noise, illumination, variable intensity and sometimes insufficient contrast. It has been observed that some of the existing methods [6–10] offer very good result for text document. However, the performance degrades when a degraded text document contains some graphical images in it. We refer this type of document as compound document in the c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved K. Saeed and W. Homenda (Eds.): CISIM 2016, LNCS 9842, pp. 105–115, 2016. DOI: 10.1007/978-3-319-45378-1 10
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rest of this paper. In this research work, we aim to devise a new segmentation methodology that would be good for the compound documents. We separate the entire image into three regions as the background, only text region and the graphical image. Our proposed method keeps a good balance both for text and graphics in the degraded compound documents. The proposed binarization method is based on cluster density information. It consists of six phases. These are noise removal with image normalization, entropy calculation, fuzzy c mean clustering, segmenting of each region based on the clustering output, applying local threshold based binarization and finally integrating the segmented region. Each of these phases is described detail in the design methodology section.
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Survey on Existing Techniques
Document image binarization has drawn lot of attention in the machine vision research community. Some of the highly cited methods are discussed in this section in a nutshell. Parker et al. proposed a method based on Shen-Castan edge detector to identify object pixels [7]. This method creates a surface using moving least squares method used to threshold images. Chen et al. proposed enhanced speed entropic threshold selection algorithm [8]. This method works upon the selection of global threshold value using maximin optimization procedure. O’Gorman proposed a global approach based on the measurement of information on local connectivity. T
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