Enhancement and cleaning of handwritten data by using neural networks

In this work, artificial neural networks are used to clean and enhance scanned images for a handwritten recognition task. Multilayer perceptrons are trained in a supervised way using a set of simulated noisy images together with the corresponding clean images for the desired output. The neural network acquires the function of a desired enhancing method. The performance of this method has been evaluated for both noisy artificial and natural images. Objective and subjective methods of evaluation have shown a superior performance of the proposed method over other conventional enhancing and cleaning filters.