There has been a massive growth in the production of various unstructured, complex and multi-lingual digitised documents in recent years. Storing and manipulating such digitised documents towards a paperless society has been the objective of emerging technology. As the human visual system can easily distinguish the global summary of images, extracting features based on human attention from images is desirable to achieve more accurate document image retrieval results. Thus, in this research work, an appearance-based document image retrieval system using image saliency maps depending on human visual attention is proposed. The saliency map obtained from the input document image is used to generate a weighted document image. Features are then extracted from the weighted document images using the Gist operator. Then, locality-sensitive hashing is considered to compute similarity distances between a query and the document images in the knowledge-based database. To evaluate the performance of the proposed document image retrieval system MTDB, ITESOFT, and CLEF-IP datasets of document images were used for experimentation. The proposed document image retrieval system provided promising retrieval results compared to the results reported in the literature.