Scene word images undergo degradations due to motion blur, uneven illumination, shadows and defocusing, which lead to difficulty in segmentation. As a result, the recognition results reported on the scene word image datasets of ICDAR have been low. We introduce a novel technique, where we choose the middle row of the image as a subimage and segment it first. Then, the labels from this segmented sub-image are used to propagate labels to other pixels in the image. This approach, which is unique and distinct from the existing methods, results in improved segmentation. Bayesian classification and Max-flow methods have been independently used for label propagation.