Automatic signboard detection and localization in densely populated developing cities

. Most city establishments of developing cities are digitally un-labeled because of the lack of automatic annotation systems. Hence location and trajectory services such as Google Maps, Uber etc remain un-derutilized in such cities. Accurate signboard detection in natural scene images is the foremost task for error-free information retrieval from such city streets. Yet, developing accurate signboard localization system is still an unresolved challenge because of its diverse appearances that include textual images and perplexing backgrounds. We present a novel object detection approach that can detect signboards automatically and is suitable for such cities. We use Faster R-CNN based localization by in-corporating two specialized pretraining methods and a run time efficient hyperparameter value selection algorithm. We have taken an incremen-tal…