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We present an approach to finding (and separating) lines of text in free-form handwritten historical document images. After preprocessing, our method uses the count of foreground/background transitions in a binarized image to determine areas of the document that are likely to be text lines. Alternatively, an Adaptive Local Connectivity Map (ALCM) found in the literature can be used for this step of the process. We then use a min-cut/max-flow graph cut algorithm to split up text areas that appear to encompass more than one line of text. After removing text lines containing relatively little text information (or merging them with nearby text lines), we create output images for each line. A grayscale output image is created, as well as a special mask image containing both the foreground and information flagging ambiguous pixels. Foreground pixels that belong to other text lines are removed from the output images to provide cleaner line images useful for further processing. While some refinement is still necessary, the result of early experimentation with our method is encouraging.

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Large-scale, multi-terabyte digital libraries are becoming feasible due to decreasing costs of storage, CPU, and bandwidth. However, costs associated with preparing content for input into the library remain high due to the amount of human labor required. This paper describes the Digital Microfilm Pipeline-a sequence of image processing operations used to populate a large-scale digital library from a "mountain" of microfilm and reduce the human labor involved. Essential parts of the pipeline include algorithms for document zoning and labeling, consensusbased template creation, reversal of geometric transformations and Just-In-Time Browsing, an interactive technique for progressive access of image content over a low-bandwidth medium. We also suggest more automated approaches to cropping, enhancement and data extraction.

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