Local Co-occurrence and Contrast Mapping for Document Image Binarization

Document Image Binarization refers to the task of transforming a scanned image of a handwritten or printed document into a bi-level representation containing only characters and background. Here, we address the historic document image binarization problem using a three-stage methodology. Firstly, we remove possible stains and noise from the document image by estimating the document background image. The remaining background and character pixels are separated using a Local Co-occurrence Mapping, local contrast and a two-state Gaussian Mixture Model. In the last stage, possible isolated misclassified blobs are removed by a morphology operator. The proposed scheme offers robust and fast performance, especially for handwritten documents.