Fast rule-line removal using integral images and support vector machines

In this paper, we present a fast and effective method for removing pre-printed rule-lines in handwritten document images. We use an integral-image representation which allows fast computation of features and apply techniques for large scale Support Vector learning using a data selection strategy to sample a small subset of training data. Results on both constructed and real-world data sets show that the method is effective for rule-line removal. We compare our method to a subspace-based method and show that better accuracy can be achieved in considerably less time. The integral-image based features proposed in the paper are generic and can be applied to other problems as well.