## 1 Introduction  
The rise in mobile phone usage for capturing digital images and the affordability of digital storage systems have shifted the focus towards visual searches instead of traditional text-based searches [1]. This shift has garnered significant interest from the academic community, leading to extensive research in general image retrieval and object recognition. However, there has been a noticeable gap in research regarding DIR with extensive datasets [2]. DIR systems find relevance when printed documents coexist with the need for supplementary digital information. For instance, mobile phones can capture images of printed documents, which can be queried to retrieve specific details. This includes determining the workflow for which the printed copy was created or identifying related documents, offering a versatile approach to access and augment published content with digital information. These systems are also crucial in education, where printed textbooks remain prevalent [3]. For example, students can use their mobile phones to capture sections, exercises, or figures they find challenging or wish to learn more about. A DIR system can then analyze these images, deduce the specific content causing difficulty, and provide additional support. This support might include online tutorials, opportunities for peer discussions, or details about problem solutions and related topics. This approach enables students to access study materials instantly without relying on textual searches. It encourages them to engage in in-depth discussions on subjects they find challenging or want to explore further. Traditionally, when students encounter difficulties or require additional information about the topics or solutions presented in these books, they turn to their teachers or peersfor assistance. However, due to time constraints, teachers often need help to address every student's query, and the quality of responses may vary. This scenario often leads to the repetition of the same problems being discussed by different teachers with different students across the country, resulting in an inefficiency [4]. We have developed a DIR system for use within a mobile application to address this challenge. The context for this application is the university entrance exams in the Turkish education system, where thousands of students widely use a standardized set of preparation books. These books contain similar problem sets and learning materials. Students can now use their mobile phones to capture images of their questions or problems. The DIR system then processes these images to check if similar queries have been asked and answered previously. The system provides an instant solution or additional information if a match is found. If not, the question is added to a pool for other teachers and students to address, reducing redundancy and promoting more efficient knowledge sharing. This innovative approach enhances the accessibility of educational resources and fosters collaborative learning among students. During our research, we have observed that relying on a single perspective, such as textual data, global image features, or spatial relations within document images, in a DIR system falls short of achieving the desired results. Each of these single-perspective DIR systems comes with its strengths and weaknesses. To address these limitations effectively, we propose the development of an ensemble of retrieval methods that consider various characteristics of both query and data images. These retrievals then rank the results based on weighted votes. We have designed retrievals with varying levels of effectiveness, termed weak and strong retrievals, depending on their capacity to search extensive document datasets. These retrievals utilize different sets of document image characteristics, such as local structural relationships within images, textual information, run-length histograms, or features extracted in proximity to words.