Existing methods for shadow removal in document images have made certain progress. However, these approaches primarily focus on the image itself and the background color information, neglecting the fact that in real-world scenarios, document images often contain shadows with varying densities. Consequently, these methods may result in issues such as loss of local details and content inconsistencies. To address these challenges, a document image shadow removal algorithm guided by density and color information is proposed. A feature extraction module is first designed to extract shadow density and background color information from the input image. Before each module receives its input, an adaptive module adjusts the initial shadow density features, which are then combined with the background color information to guide the subsequent shadow removal process. To better extract both global and local features, a density- and color-guided Transformer module, along with a shadow-sensitive local feature extraction module, is proposed. The two modules are combined to form the primary shadow removal module. Experimental results demonstrate that, compared to current methods, the proposed network model outperforms existing text image shadow removal techniques.