Text area segmentation from document images by novel adaptive thresholding and template matching using texture cues

This paper presents a new perspective of text area segmentation from document images using a novel adaptive thresholding for image enhancement. Using sliding windows, the texture of the enhanced image is matched with that of a fixed training template image containing the typed letters ‘dB.’ The affine-invariant, low-dimensional difference theoretic texture feature set is used for the texture measurement. The distance matrix is binarized using Otsu threshold, and the ‘0’ pixels indicate the text area. One primary contribution of this paper is the novel adaptive thresholding for document image enhancement prior to the extraction of texture cues. The proposed adaptive thresholding mimics the ability of the human eye to iteratively adjust to varying light intensities through iterative gamma correction followed by contrast stretching so that the text becomes well defined against the background clutter. The text blobs so segmented are binarized using Yanowitz and Bruckstein method of text binarization, and the results are applied for evaluation with respect to the ground-truth annotations. We tested our algorithm on the benchmark DIBCO 2009, 2010, 2011, 2012, 2013 document image datasets in comparison with the state of the art. The high precision–recall and F -score values establish the efficiency of our approach.