This paper describes a proposed document analysis system that aims at automatic indexing of digitized images of old newspaper microfilms. This is done by extracting news headlines images of old newspaper microfilms. This is done by extracting news headlines from microfilm images. The headlines are then converted to machine readable text by OCR to serve as indices to the respective news articles. A major challenge to us is the poor image quality of the microfilm as most images are usually inadequately illuminated and considerably dirty. To overcome the problem we propose a new effective method for separating characters from noisy background since conventional threshold selection techniques are inadequate to deal with these kinds of images. A Run Length Smearing Algorithm (RLSA) is then applied to the headline extraction. Experimental results confirm the validity of the approach.
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