Local-spectrum-based distinction between handwritten and machine-printed characters

In this paper, we propose a method to distinguish between handwritten and machine-printed characters with no need to locate character or text-line positions. We transform a local region in a document image into frequency domain to extract feature values including fluctuations caused by handwriting. We feed the feature values to an optimized multilayer perceptron (MLP) to get likelihood of handwriting. We call this method the spectrum-domain local fluctuation detection (SDLFD) method. Experimental results show that our method distinguishes handwritten characters from machine-printed ones with no need of text-line position information. We also found that the scheme is robust against the change in scanning resolution.