REMOVE NOISE EFFECTS FROM DEGRADED DOCUMENT IMAGES USING MATLAB ALGORITHM

Digital images are subjected to blurring due to many hardware limitations such as atmospheric disturbance, device noise and poor focus quality. In order to reveal the detailed information carried in the digital image, image de-blurring or restoration is necessary. Image de-blurring have wide applications, from consumer photography, e.g., remove motion blur due to camera shake, to radar imaging and tomography, e.g., remove the effect of imaging system response. This research is aimed to provide a basic knowledge of image degradation and restoration process. Offline handwriting recognition approaches proceed by segmenting characters into smaller pieces which are recognized separately. The recognition result of a word is then the composition of the individually recognized parts. Inspired by results in cognitive psychology, researchers have begun to focus on holistic word recognition approaches. Here we present a holistic word recognition approach for degraded documents, which is motivated by the fact that for severely degraded documents a segmentation of words into characters will produce very poor results. The quality of the original documents does not allow us to recognize them with high accuracy - our goal here is to produce transcriptions that will allow successful retrieval of images, which has been shown to be feasible even in such noisy environments. We believe that this is the first systematic approach to recognizing words in historical manuscripts with extensive experiments. Our experiment is to clear the degraded documents using some filter approach. We will use wiener filter for removing noise partials from different images using wiener filter algorithm.