An End-to-End Multi-modal-based Framework for Visual Identity Inspection System

Visual identity (VI) comprises symbols and text elements that express the essence of the corporate. Misuse of the visual identity could significantly harm the corporate image and reputation. Therefore, it is important to early detect the misuse of visual identity and take the proper measurements. However, it is challenging to execute the inspection manually, especially for large-scale corporate in the modern era. In this paper, we introduce an end-to-end framework to manage an inspection system based on deep learning technique intending to automatically detect the misuse of corporate VI in various forms of media. More precisely, based on the characteristics of corporate logos in VI, we propose a novel method to systematically generate synthetic dataset, which is used to train the logo detection model. Furthermore, with the general common visual feature of logo and OCR algorithm, a robust rules-based engine is designed to automatically discriminate the VI misuse by the input image. Overall, the case study shows a precision of 90% and recall of 85%, and total processing time of each image could be less than 0.5 second.