The use of digital images of scanned handwritten historical documents has increased in recent years, especially with the online availability of large document collections. However, the sheer number of images in some of these collections makes them cumbersome to manually read and process, making the need for automated processing of increased importance. A key step in the recognition and retrieval of such documents is binarization, the separation of document text from the page's background. Binarization of images of historical documents that have been affected by degradation or are otherwise of poor image quality is difficult and continues to be a topic of research in the field of image processing. This paper presents a novel approach to this problem, including two primary variations. One combines a recursive extension of Otsu thresholding and selective bilateral filtering to allow automatic binarization and segmentation of handwritten text images. The other also builds on the recursive Otsu method and adds improved background normalization and a post-processing step to the algorithm to make it more robust and to perform adequately even for images that present bleed-through artifacts. Our results show that these techniques segment the text in historical documents comparable to and in some cases better than many state-of-the-art approaches based on their performance as evaluated using the dataset from the recent ICDAR 2009 Document Image Binarization Contest.