Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. The proposed approach mainly includes two steps. Firstly, a clustering method is employed to segment candidate characters by integrating spatial connectivity and color feature of characters� pixels. In most images, colors of pixels in one character are commonly non-uniform due to the noise. So a new method is adopted in this step to obtain the color thresholds of characters. Secondly, priori knowledge and region-based method are performed on the candidate characters to filter the non-characters.

//Source code is available on request citing proper explanations.