In document forensics, identifying ink mismatches is crucial for detecting forgeries and determining document authenticity. However, identifying and separating specific inks from paper can be challenging. Hyperspectral imaging can capture unique spectral patterns exhibited by inks composed of different materials, even when their colors appear identical. Hyperspectral document analysis (HSDI) can be employed to authenticate documents by analyzing the ink. Earlier studies had insufficiently accurate black ink detection, so this paper proposes a novel approach using deep learning and pre-processing methods to improve black ink detection accuracy. The proposed approach uses supervised deep learning to identify ink mismatches in hyperspectral document images. The study evaluated the performance of the proposed model using a hyperspectral image dataset consisting of UWA (University of Western Australia) writing ink in both blue and black colors and different types of artificially identical color inks mixed in various ratios to find ink mismatches. The results showed that the proposed system performed better than other systems reported in the literature, improving the average accuracy by up to 0.18% for blue and 0.36% for black. The proposed method accurately detects ink mismatches and identifies various inks based on their unique spectral response, rendering it highly beneficial for applications in document forensics.