In this article, we present a method for identifying image reuse in a corpus of 358 books printed between the 15th and 17th century. The approach is based on image hashing, an established method for finding near duplicates of images. Our historical interpretation of the method's result produces two important insights hinting at a radical material and epistemological change taking place around 1530. We then evaluate the image hash approach against a method that employs a neural network for image recognition.
In the present work we show how many scientific illustrations of the early modern period can be used to track the evolution of visual knowledge and to detect historical communities involved in the production of the editions analyzed. In particular, we define three sorts of historically meaningful similarities among scientific illustrations, we show how such illustrations can be extracted from the sources and then clustered by means of fully computer-based methods, and finally we conclude with an example to show the potential of our approach.
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