Abstract
Many factors can influence the process of detecting and classifying stores based on their visual appearance. Previous studies built models that considered the whole storefront however, the detection and classification results were negatively impacted because of the lack of consistency in storefront design. This research focuses on store signboards as they are much more consistent. A complete framework is provided in which it enables existing real-time object detectors to integrate with another model connected to an OCR and then classify shops using NLP techniques. The models were trained and evaluated utilizing the ShoS dataset which was collected from Google Street Views for different research purposes. A total of 10k storefront signboards were captured and fully annotated. The outcomes of different baseline methodology and applications on the ShoS dataset are provided to measure the performance of our work.