A method for page segmentation for use in an optical character recognition process to detect one or more textual objects and image objects in an input de-skewed gray scale image, the method comprising: creating an edge space from the gray scale image where each pixel is declared as either an edge or non-edge pixel; identifying connected components in the edge space; applying central line tracing to the connected components to generate one or more native line candidates from the connected components, wherein each native line candidate is a set of horizontally neighboring connected components of the gray scale image in the edge space with similar vertical statistics defined with baseline and mean line values; and detecting textual objects in the image by classifying the native line candidates as textual lines or non-textual lines, wherein each native line candidate classified as a textual line is a textual object; the method characterized by : determining a threshold value defining a maximal local background variance, wherein the maximal local background variance is extracted from a histogram of local variance values created from pixels of the gray scale image located in the detected textual objects; generating a classification image of the gray scale image by performing pixel based classification using the threshold value to identify background pixels and image region pixels; identifying connected components of the image region pixels; and detecting image objects as connected components of the image region pixels having a size greater than a threshold value. creating an edge space from the gray scale image where each pixel is declared as either an edge or non-edge pixel; identifying connected components in the edge space; applying central line tracing to the connected components to generate one or more native line candidates from the connected components, wherein each native line candidate is a set of horizontally neighboring connected components of the gray scale image in the edge space with similar vertical statistics defined with baseline and mean line values; and detecting textual objects in the image by classifying the native line candidates as textual lines or non-textual lines, wherein each native line candidate classified as a textual line is a textual object; the method characterized by : determining a threshold value defining a maximal local background variance, wherein the maximal local background variance is extracted from a histogram of local variance values created from pixels of the gray scale image located in the detected textual objects; generating a classification image of the gray scale image by performing pixel based classification using the threshold value to identify background pixels and image region pixels; identifying connected components of the image region pixels; and detecting image objects as connected components of the image region pixels having a size greater than a threshold value. determining a threshold value defining a maximal local background variance, wherein the maximal local background variance is extracted from a histogram of local variance values created from pixels of the gray scale image located in the detected textual objects; generating a classification image of the gray scale image by performing pixel based classification using the threshold value to identify background pixels and image region pixels; identifying connected components of the image region pixels; and detecting image objects as connected components of the image region pixels having a size greater than a threshold value. <EOS>