Document image binarization using local features and Gaussian mixture modeling

• Background removal technique based on adaptive median filtering and thresholding
• A Local Co-occurrence Map with local contrast can distinguish between document text and document stains and background.
• Low complexity approach with fast and accurate binarization results

In this paper, we address the document image binarization problem with a three-stage procedure. First, possible stains and general document background information are removed from the image through a background removal stage. The remaining misclassified background and character pixels are then separated using a Local Co-occurrence Mapping, local contrast and a two-state Gaussian Mixture Model. Finally, some isolated misclassified components are removed by a morphology operator. The proposed scheme offers robust and fast performance, especially for both handwritten and printed documents, which compares favorably with other binarization methods.