ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 4, April 2014 Copyright to IJIRCCE www.ijircce.com 4012 Stroke and Contrast Enhancement of Degraded Document Image through Binarization Vibhavi R.N. 1 , Vrushali Uttarwar 2 M.Tech student, Department of CSE, AMCEC, Bangalore, Karnataka, India 1 Assistant Professor, Department of CSE, AMCEC, Bangalore, Karnataka, India 2 ABSTRACT: Text Segmentation from a degraded document images is a very difficult task as the document image might contain lot of variations between the foreground and the background part.Binarization is been into intense research during the last few years. Most of the developed algorithms depend on statistical methods and do not consider the nature of document images. However, recent developments call for more specialized binarization techniques. Adaptive image contrast is used as a binarization technique in this paper . The adaptive image contrast is a combination of the local image contrast and the local image gradient. It is also tolerant towards variations caused due to degradations.The proposed technique constructs an adaptive contrast for an input degraded document image. The contrast map is then binarized and combined with Canny’s edge map to identify the text stroke edge pixels. A local threshold is estimated based on the intensities of detected text stroke edge pixels within a local window and this threshold is used for segmentation purpose.. The proposed method is simple, robust, and involves minimum parameters. KEYWORDS: Adaptive image contrast, document analysis, document image processing, degraded document image binarization, pixel classification. I. INTRODUCTION Document image binarization plays a key role in document processing since its performance affects the degree of success in subsequent character segmentation and recognition. in general, image binarization is categorised in two main classes: (i) global and (ii) local. binarization is a preprocessing stage for document analysis and it is used to segment the foreground text from the document background. this technique ensures faster and accurate document image processing tasks. Most document analysis algorithms are built based on underlying binarized image data. The use of bi- level information decreases the computational load and helps in using simplified analysis methods compared to 256 levels of grey-scale or colour image information. Document image understanding methods require logical and semantic content preservation for thresholding.though document image binarization has been studied for many years, the thresholding of images is still a challenging task due to the high variation between the text stroke and the document background. for an input image, some processing stages should be used before the text extraction. one of the step includes binarization. In this stage the grey-scale image converts into a binary image. a binary image can be processed better than a grey- scale image as illustrated in fig. 1, the handwritten text within the degraded documents might contain a certain amount of variations like stroke width, stroke brightness, stroke connection, and document background. in addition, historical documents are often degraded by the bleed through, where the ink of the other side seeps through to the front. Also they are often degraded by different types of imaging artifacts. These different types of document degradations induce the document thresholding error and make degraded document image binarization very difficult. This paper presents a document binarization technique that extends previous local maximum-minimum method [1]. Fig. 1 Binarization example