Improving OCR Accuracy by Applying Enhancement Techniques on Multispectral Images
Fabian Hollaus, Markus Diem, Robert Sablatnig · 2014
This work is concerned with the legibility enhancement of ancient and degraded handwritings. The writings are partially barely visible under normal white light and hence they have been imaged with a MultiSpectral Imaging (MSI) system in order to increase their legibility. Dimension reduction techniques - like Principal Component Analysis (PCA) - can be used to further enhance the contrast of the faded-out characters. In this work the dimensionality of the multispectral scan is lowered, by applying Linear Discriminant Analysis (LDA). Since LDA is a supervised dimension reduction method, it is necessary to label a subset of the multispectral samples as belonging to the fore-or background. For this purpose, an approach is suggested that uses spatial information. The enhancement method is evaluated by Optical Character Recognition (OCR). By applying the enhancement method the OCR performance is increased in the case of degraded writings, compared to OCR results gained on unprocessed multispectral images and to OCR results achieved on images, which have been produced by applying unsupervised dimension reductions.