Document images often suffer from different types of degradation that renders the document image binarization is a challenging task. Document image binarization is of great significance in the document image analysis and recognition process because it affects additional steps of the recognition development. The Comparison of image gradient and image contrast is estimated by the local maximum and minimum which has high quality. So, it is more tolerant to the rough lighting and other types of document degradation such as low contrast images and partially visible images. The distinction between the foreground text and the background text of different document images is a difficult task. This paper presents a new document image binarization technique that focus on these issues using adaptive image contrast. The grouping of the local image contrast and the local image slope is the adaptive image contrast so as to tolerate the text and surroundings distinction caused by dissimilar types of text degradations. In image binarization technique, the construction of adaptive contrast map is done for an input degraded document image which is then adaptively binarized and combined with Canny’s edge detector to recognize the text stroke edge pixels. The document text is advance segmented by means of a local threshold. We try to apply the self-training adaptive binarization approach on existing binarization methods, which improves not only the performance of existing binarization methods, but also the, toughness on different kinds of degraded document images.
DOI: 10.17762/ijritcc2321-8169.15066
