This paper will address a recent project at Kent State University to adapt the interface of Special Collections and Archives Digital Collections to better reflect textual archival content. As our project team discovered, some of these changes also improved ingestion workflows and the quality of the optical character recognition as a result. Automated processes of creating PDF files and updating related finding aid pages will also be discussed. The visual presentation during the 2017 ASIST Annual Meeting will provide visitors the opportunity to view the digital collections and interact directly with the project through the live library website as outlined in this paper. The project to date is based on open source technologies, and our team actively works to share code back to the respective community.