The detection of copy–move forgeries has been studied extensively, however all known methods were designed and evaluated for digital images depicting natural scenes. In this paper, we address the problem of detecting and localizing copy–move forgeries in images of scanned text documents. The purpose of our analysis is to study how block-based detection of near-duplicates performs in this application scenario considering that even authentic scanned text contains multiple, similar-looking glyphs (letters, numbers, and punctuation marks). A series of experiments on scanned documents is carried out to examine the operation of some feature representations proposed in the literature with respect to the correct detection of copied image segments and the minimization of false positives. Our findings indicate that, subject to specific threshold and parameter values, the block-based methods show modest performance in detecting copy–move forgery from scanned documents. We explore strategies to further adapt block-based copy–move forgery detection approaches to this relevant application scenario.