Data de-duplication is the process of detecting records in one or more datasets which refer to the same entity. In this paper we illustrate the relationships between de-duplication problems and capture-recapture models. In particular we tackle the de-duplication process via a latent entity model, where the observed data comprise clusters of perturbed versions of a set of key variables drawn from a finite population of N different entities. We  consider the population size N as an unknown model parameter. As a result, we are able to account for the de-duplication uncertainty in the population size estimation. We apply our method to two synthetic data sets comprising German names. In addition we illustrate a real data application, where we match records from two lists which report information about people killed in the recent Syrian conflict.