A DNN Framework For Text Image Rectification From Planar Transformations
This work addresses text image rectification for computer vision applications, presenting an incremental improvement with a new dataset and method.
The paper tackles the problem of rectifying text images under planar transformations by proposing a novel neural network architecture that learns geometric transformations without explicit segmentation supervision, achieving robust and effective restoration as demonstrated on a new public dataset.
In this paper, a novel neural network architecture is proposed attempting to rectify text images with mild assumptions. A new dataset of text images is collected to verify our model and open to public. We explored the capability of deep neural network in learning geometric transformation and found the model could segment the text image without explicit supervised segmentation information. Experiments show the architecture proposed can restore planar transformations with wonderful robustness and effectiveness.