![]() Īnother goal of digital image processing of a manuscript is to prepare it for automatic word and character recognition. In this sense, the plurality of the content of the manuscript should be analyzed and discriminated, in such a way as to be able to preserve and highlight the useful patterns, and remove the extra, useless patterns that can disturb or even make impossible the academic study. Therefore, virtual restoration algorithms are required that attempt to restore the manuscripts to their original appearance, eliminating only the degradation without destroying the other informative characteristics. In addition to the main text and paper texture, they may contain other informational elements, such as annotations, thumbnails, stamps, or non-informative interferences due to damage, such as damp and mold stains, or ink infiltrations from the back.Īn important goal of digital image processing techniques is to provide scholars with digital versions that can help them in their work of reading, transcription and interpretation. Typically these ancient manuscripts appear as the overlay of a number of different patterns, or layers of information. Historical and archival manuscripts are almost always damaged by the natural degradation of the materials over time or by other accidental factors such as fires, floods and poor preservation. We compare the performance of this NN and other methods both qualitatively and quantitatively on a reference dataset and heavily damaged historical manuscripts. More explicitly, the network can be trained without the need for a large class of other similar manuscripts, but is still able, at least to some extent, to classify manuscripts with varying degrees of corruption. By virtue of the parametric nature of the model, various levels of damage can be simulated in the training set, favoring a generalization capability of the NN. We adopt a very simple shallow NN whose learning phase employs a training set generated from the data itself using a theoretical blending model that takes into account ink diffusion and saturation. A procedure based on neural networks (NN) is proposed here to clean up the complex background of the manuscripts from this interference. ![]() This is a very impairing damage that cannot be physically removed, and hinders both the work of philologists and palaeographers and the automatic analysis of linguistic contents. In historical recto–verso manuscripts, very often the text written on the opposite page of the folio penetrates through the fiber of the paper, so that the texts on the two sides appear mixed. ![]()
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