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Existing binarization methods are categorized as either global or local. In this paper, we present a new category, where the image is considered a collection of subimages. Each subimage provides a statistical model for the handwritten characters that can be used to optimize the binarization of other subimages based on gray-level and stroke-run features. The proposed method uses these multimodels to iteratively arrive at the optimal threshold for each subimage. It can be applied to different types of documents where prior knowledge about the noisiness of the subimages is not available. Experimental results showed significant improvement in the binarization quality in comparison with other well-established algorithms.