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"Text can appear anywhere". This property requires us to carefully processall the pixels in an image in order to accurately localize all text instances.In particular, for the more difficult task of localizing small text regions,many methods use an enlarged image or even several rescaled ones as theirinput. This significantly increases the processing time of the entire image andneedlessly enlarges background regions. If we were to have a prior telling usthe coarse location of text instances in the image and their approximate scale,we could have adaptively chosen which regions to process and how to rescalethem, thus significantly reducing the processing time. To estimate this priorwe propose a segmentation-based network with an additional "scale predictor",an output channel that predicts the scale of each text segment. The network isapplied on a scaled down image to efficiently approximate the desired prior,without processing all the pixels of the original image. The approximated prioris then used to create a compact image containing only text regions, resized toa canonical scale, which is fed again to the segmentation network forfine-grained detection. We show that our approach offers a powerful alternativeto fixed scaling schemes, achieving an equivalent accuracy to larger inputscales while processing far fewer pixels. Qualitative and quantitative resultsare presented on the ICDAR15 and ICDAR17 MLT benchmarks to validate ourapproach.

It's All About The Scale -- Efficient Text Detection Using Adaptive Scaling