Application of The Speed-Up Robust Features Method To Identify Signature Image Patterns On Single Board Computer

Through the development of a signature pattern recognition program on SBC Beagle-bone Black, this research seeks to determine how to differentiate between real and false signatures. Three techniques of gathering data were employed in this study: interviews, observations, and a review of the literature. The quick application development method is the approach that is applied. The rapid, efficient, and brief development cycle (RAD) is emphasized. This study uses a use-case diagram to illustrate the application's logic and data flow. In this study, OpenCV is used as a digital image processing library along with the C++ programming language and QT creator as an integrated development environment (IDE). This application was subjected to both accuracy and functional testing. The following conclusions are drawn from the findings of the investigation and testing that was done: Using the fast library approach for approximate nearest neighbors (FLANN) and the speeded-up robust features (SURF) feature extraction method, the signature pattern recognition program on the Beagle-bone black SBC can differentiate between real and fraudulent signatures. Through the processes of generating image scale space, feature localization, and feature description, the SURF approach extracts feature from signature images. This signature pattern recognition application is one of the digital image processing apps that can be run on the Beagle-bone Black single board computer. This indicates that the specifications of the SBC Beagle-bone Black for digital image processing are good.