  Understanding the meaning of text in images of natural scenes like highway
signs or store front emblems is particularly challenging if the text is
foreshortened in the image or the letters are artistically distorted. We
introduce a pipeline-based text spotting framework that can both detect and
recognize text in various fonts, shapes, and orientations in natural scene
images with complicated backgrounds. The main contribution of our work is the
text detection component, which we call UHT, short for UNet, Heatmap, and
Textfill. UHT uses a UNet to compute heatmaps for candidate text regions and a
textfill algorithm to produce tight polygonal boundaries around each word in
the candidate text. Our method trains the UNet with groundtruth heatmaps that
we obtain from text bounding polygons provided by groundtruth annotations. Our
text spotting framework, called UHTA, combines UHT with the state-of-the-art
text recognition system ASTER. Experiments on four challenging and public
scene-text-detection datasets (Total-Text, SCUT-CTW1500, MSRA-TD500, and
COCO-Text) show the effectiveness and generalization ability of UHT in
detecting not only multilingual (potentially rotated) straight but also curved
text in scripts of multiple languages. Our experimental results of UHTA on the
Total-Text dataset show that UHTA outperforms four state-of-the-art text
spotting frameworks by at least 9.1 percent points in the F-measure, which
suggests that UHTA may be used as a complete text detection and recognition
system in real applications.
