Patient motion artifacts are often visible in densely sampled or large wide field-of-view (FOV) retinal optical coherence tomography (OCT) volumes. A popular strategy for reducing motion artifacts is to capture two orthogonally oriented volumetric scans. However, due to larger volume sizes, longer acquisition times, and corresponding larger motion artifacts, the registration of wide FOV scans remains a challenging problem. In particular, gaps in data acquisition due to eye motion, such as saccades, can be significant and their modeling becomes critical for successful registration. In this article, we develop a complete computational pipeline for the automatic motion correction and accurate registration of wide FOV orthogonally scanned OCT images of the human retina. The proposed framework utilizes the retinal boundary segmentation as a guide for registration and requires only a minimal transformation of the acquired data to produce a successful registration. It includes saccade detection and correction, a custom version of the optical flow algorithm for dense lateral registration and a linear optimization approach for axial registration. Utilizing a wide FOV swept source OCT system, we acquired retinal volumes of 12 subjects and we provide qualitative and quantitative experimental results to validate the state-of-the-art effectiveness of the proposed technique. The source code corresponding to the proposed algorithm is available online.