  Binarization is a well-known image processing task, whose objective is to
separate the foreground of an image from the background. One of the many tasks
for which it is useful is that of preprocessing document images in order to
identify relevant information, such as text or symbols. The wide variety of
document types, alphabets, and formats makes binarization challenging. There
are multiple proposals with which to solve this problem, from classical
manually-adjusted methods, to more recent approaches based on machine learning.
The latter techniques require a large amount of training data in order to
obtain good results; however, labeling a portion of each existing collection of
documents is not feasible in practice. This is a common problem in supervised
learning, which can be addressed by using the so-called Domain Adaptation (DA)
techniques. These techniques take advantage of the knowledge learned in one
domain, for which labeled data are available, to apply it to other domains for
which there are no labeled data. This paper proposes a method that combines
neural networks and DA in order to carry out unsupervised document
binarization. However, when both the source and target domains are very
similar, this adaptation could be detrimental. Our methodology, therefore,
first measures the similarity between domains in an innovative manner in order
to determine whether or not it is appropriate to apply the adaptation process.
The results reported in the experimentation, when evaluating up to 20 possible
combinations among five different domains, show that our proposal successfully
deals with the binarization of new document domains without the need for
labeled data.
