COMPARATIVE ANALYSIS OF FILTERS FOR TEXT EXTRACTION FROM NOISY IMAGES

With rapid growth of the internet the amount of image and video data is increasing exponentially. The text data present in images and videos is useful for automatic annotations, indexing and structuring of images. There is huge increment in images and video database online. In such database, there is need to fetch, explore an d inspect the images and videos. Text extraction plays a major role in finding vital and valuable information. Noise is an important factor that influences the quality of image which is mainly produced in the processes of image acquirement and transmission . An image can be contaminated by noise like salt and pepper noise, random valued impulse noise, speckle noise and Gaussian noise. For the removal of noise from images, the filtering algorithm like adaptive filter, average filter, maximum filter, median fi lter, minimum filter, trimmed filter and wiener filter are used. After removing noise from input complex image the text is extracted in binary form through proposed algorithm. The proposed method uses the techniques of local contrast, local gradient, adapt ive map contrast, canny edge detection for detection of text strokes and Otsu threshold for calculation of threshold value .On the basis of calculated threshold value the pixels are classified into background and foreground .A comparative study of some pop ular existing filtering method is done for text extraction from complex images .The proposed method is simulated in MATLAB to verify and va lidate the performance analysis .