Historical Handwritten Document Image Segmentation Using Background Light Intensity Normalization

This paper presents a new document binarization algorithm for camera images of historical handwritten documents, which are especially found in The Library of Congress of the Unite States. The algorithm uses two background light intensity normalization algorithms to enhance the images before a local adaptive binarization algorithm is applied. The image normalization algorithms uses adaptive linear and non-linear functions to approximate the uneven background of the images due to the uneven surface of the document paper, aged color and light source of the cameras for image lifting. Our algorithms adaptively captures the background of a document image with a ”best fit” approximation. The document image is then normalized with respect to the approximation before a thresholding algorithm is applied. The technique works for both gray scale and color historical handwritten document images with significant improvement in readability for both human and OCR.