Images of historical documents are sensitive to the significant degradation over time. Due to this degradation, exploiting information contained in these documents has become a challenging task. Consequently, it is important to develop an efficient tool for the quality enhancement of such documents. To address this issue, we present in this paper a new model known as EHDI (Enhancement of Historical Document Images) which is based on generative adversarial networks. The task is considered as an image-to-image conversion process where our GAN model involves establishing a clean version of a degraded historical document. EHDI implies a global loss function that associates content, adversarial, perceptual and total variation losses to recover global image information and generate realistic local textures. Both quantitative and qualitative experiments demonstrate that our proposed EHDI outperforms significantly the state-of-the-art methods applied to the widespread DIBCO 2013, DIBCO 2017, and H-DIBCO 2018 datasets. Our suggested model is adaptable to other document enhancement problems, following the results across a wide range of degradations. Our code is available at https://github.com/Abir1803/EHDI.git.