Signal & Image Processing : An International Journal (SIPIJ) Vol.5, No.2, April 2014 DOI : 10.5121/sipij.2014.5203 29            Rajesh K. Bawa 1 and Ganesh K. Sethi 2 1 Department of Computer Science, Punjabi University, Patiala, India 2 M.M. Modi College, Patiala, India ABSTRACT This paper presents a binarization method for camera based natural scene (NS) images based on edge analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to remove edges corresponding to non-text regions. The image is binarized using mean and standard deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes. The algorithm is tested on a variety of NS images captured using a digital camera under variable resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are compared with other standard techniques. The method is fast and works well for camera based natural scene images. KEYWORDS Text information extraction (TIE), Global Thresholding, Local Thresholding, Canny edge detection, Morphological dilation 1. INTRODUCTION Extracting text from camera based natural scene images is very challenging problem due to variation in font size, style, complex backgrounds, shadows, reflections from background surface, and uneven lightening conditions. To extract the textual information from the images, the images is segmented into its constituent components and the text part is separated from nontext part. For segmenting the image, the first step applied is image thresholding or image binarization. This step is very crucial as the success of complete method depends upon it. A large number of techniques have been given in the literature. A survey of binarization techniques for nondestructive testing images and document images was given by Sezgin and Sankur [3] in which various techniques were divided into six categories: histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. No technique fits well for all type of images has every technique has its pros and cons. Binarization of camera based natural scene images for the purpose of text extraction is a challenging task due to complex backgrounds, uneven lighting conditions, shadows, different types of text (font, style,