This paper presents a technique for document image binarization that segments the foreground text accurately from poorly degraded document images. The proposed technique is based on the Segmentation of text from poorly degraded document images and it is a very demanding job due to the high variation between the background and the foreground of the document. This paper proposes a novel document image binarization technique that segments the texts by using adaptive image contrast. It is a combination of the local image contrast and the local image gradient that is efficient to overcome variations in text and background caused by different types degradation effects. In the proposed technique, first an adaptive contrast map is constructed for a degraded input document image. The contrast map is then binarized by global thresholding and pooled with Canny’s edge map detection to identify the text stroke edge pixels. By applying Segmentation the text is further segmented by a local thresholding method that. The proposed method is simple, strong, and requires minimum parameter tuning.