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An end-to-end system for text detection and recognition is important in multiple domains such as content based retrieval systems, video event detection, human computer interaction, autonomous robot or vehicle navigation and vehicle license plate recognition. There are several commercial systems for text recognition in scanned document. However, these systems typically need cropped and binarized text regions to perform well for natural scene text. Text detection in natural scenes is a challenging problem and has gained a lot of attention recently. Such texts present low contrast with background, large variation in font, color, scale and orientation combined with background clutter. Therefore a robust and fast recognition system is desirable. In this work, we have explored the dominant text detection in video frames in which variety of images has been selected which has arbitrary alignment of text as well as varying color and size of the text. In this work, we have used wavelet based laplacian filters as well as laplacian of Gaussian filters to get the arbitrary directed text edges in a frame. After that fuzzy clustering has been applied to get the foreground text edges on which dilation and erosion operations are applied to get the pixels containing whole text. After that MSER technique has been implemented along with connected component analysis to extract the prominent text pixels in the image. Experimental results show that the present technique gives good recall and precision ratio on the tested databases. KeywordsImage, Pixels Research Paper for Arbitrary Oriented Team Text Detection in Video Images Using Connected Component Analysis 138 INDENTATIONS AND EQUATIONS An image is an array or a matrix of square pixels (picture elements) arranged in columns and rows. In a (8-bit) grayscale image, each picture element has an assigned intensity that ranges from 0 to 255. A grayscale image is what people normally call a black and white image but the name emphasizes that such an image will also include many shades of gray. A normal grayscale image has 8 bit color depth = 256 grayscales. A "true color" image has 24 bit color depth = 8 x 8 x 8 bits = 256 x 256 x 256 colors = ~16 million colors. Some grayscale images have more grayscales, for instance 16 bit = 65536 grayscales. An image may be defined as a two-dimensional function f(x, y), where x and y are spatial (plane) coordinates and the amplitude off(x,y) at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y and amplitude values of f are all finite, discrete quantities, then the image is called a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels or pixels. Pixel is the term most widely used to denote the elements of a digital image. A digital Image processing start with one image and produces a modified version of that image [1]. Digital image analysis is a process that transforms a digital image into something other than a digital image, such as a set of measurement data, alphabet text or a decision. Image digitization is a process that converts a pictorial form to numerical data.

Research paper for arbitrary oriented team text detection in video images using connected component analysis