Deep transformer: A framework for 2D text image rectification from planar transformations

In this paper, a novel neural network architecture is proposed to rectify text images with mild assumptions. A new dataset of text images is collected to verify our model. We explored the capability of deep neural network in learning geometric transformation and found the model are sensitive to the text image without explicit supervised segmentation information. Experiments show the architecture proposed can restore planar transformations with wonderful robustness and effectiveness.