<![CDATA[Digitalization of documents is an important necessity for supporting more efficient, practical, and easily accessible archive storage through mobile devices. However, document scanning using a smartphone camera still frequently produces suboptimal images due to lighting conditions, image-capture angle, and perspective distortion. This research develops the Android-based application Digital Portable Archive and Document Assistant (D-PANDA) by integrating the Google ML Kit Document Scanner API for automatic document detection and OpenCV for image quality enhancement, with a PHP Native backend and MariaDB database. Unlike commercial black-box solutions, D-PANDA provides full algorithmic transparency through its documented OpenCV-based post-processing pipeline, and offers a self-hosted backend alternative for organizations prioritizing data sovereignty. Testing was conducted using Black Box Testing to verify that all application functions operate according to requirements, as well as the User Experience Questionnaire Short (UEQ-S) with 15 respondents to evaluate the user experience. Black Box testing results indicate that all application features function correctly, with no functional errors detected. Meanwhile, the UEQ-S evaluation yielded scores of Pragmatic Quality = 2.232, Hedonic Quality = 2.286, and Overall = 2.259, all in the Excellent category, indicating that the application ranks among the top 10% of results according to the UEQ-S benchmark. The research findings demonstrate that D-PANDA effectively supports document digitalization and management while delivering a very positive user experience.]]>
