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Most city establishments of developing cities are digitally unlabeled becauseof the lack of automatic annotation systems. Hence location and trajectoryservices such as Google Maps, Uber etc remain underutilized in such cities.Accurate signboard detection in natural scene images is the foremost task forerror-free information retrieval from such city streets. Yet, developingaccurate signboard localization system is still an unresolved challenge becauseof its diverse appearances that include textual images and perplexingbackgrounds. We present a novel object detection approach that can detectsignboards automatically and is suitable for such cities. We use Faster R-CNNbased localization by incorporating two specialized pretraining methods and arun time efficient hyperparameter value selection algorithm. We have taken anincremental approach in reaching our final proposed method through detailedevaluation and comparison with baselines using our constructed SVSO (StreetView Signboard Objects) signboard dataset containing signboard natural sceneimages of six developing countries. We demonstrate state-of-the-art performanceof our proposed method on both SVSO dataset and Open Image Dataset. Ourproposed method can detect signboards accurately (even if the images containmultiple signboards with diverse shapes and colours in a noisy background)achieving 0.90 mAP (mean average precision) score on SVSO independent test set.Our implementation is available at:https://github.com/sadrultoaha/Signboard-Detection

Automatic Signboard Detection and Localization in Densely Populated Developing Cities