DocLightDetect: A New Algorithm for Occlusion Classification in Identification Documents

Ricardo Batista das Neves Junior, Byron Leite Dantas Bezerra, Cleber Zanchettin

Abstract: In the current digital era, organizations primarily interact with their clients and users online. However, accurately identifying these digital users in the physical realm raises significant challenges. Several entities, including financial institutions, insurance companies, and government services, require photos of documents sent through mobile applications to associate the physical and digital personas. This procedure entails significant computational challenges, mainly due to the need for adequate user guidance when capturing images and the variability of devices. User dependence often results in occlusions in images caused by various factors such as human fingers, shadows, and the spotlight effect. The latter is particularly common and complex due to using the device’s flash. While previous research has focused on automatically identifying occlusions caused by human fingers, the present work focuses on occlusions caused by the spotlight effect. We propose a new algorithm, DocLightDetect, which uses image segmentation as a preprocessing step to improve the accuracy of classifying occlusions caused by the spotlight effect in identification documents. The effectiveness of DocLightDetect is demonstrated through the new SpotBID Set dataset. The proposed algorithm improves performance compared to state-of-the-art document occlusion classification techniques. It is also optimized for low computational cost, making it suitable for applications in mobile devices, robotics, and the Internet of Things (IoT).