The growing number of textual reports poses a great challenge for investigative analysis. However, text visualization has the potential to address this problem by automating the analysis of text reports, thus reducing workloads and providing new insights for crime analysts. We are developing a crime report visualization system for such investigative analysis. Our system leverages natural language processing and visualization techniques to enhance decision support. To measure its usefulness, we conducted a case study to compare the use of our system with a traditional paper-based approach to identify crime reports discussing the same crime. The crime analyst discovered 4 out of 5 crime reports correctly among 40 reports using either the paper-based or system-based approach. However, less time was spent on the tasks using our system compared to the paper-based approach. Our interface and visualization components such as the highlighting function were rated positively by the crime analyst.