Document Image Dewarping using Deep Learning

The distorted images have been a major problem for Optical Character Recognition (OCR). In order to perform OCR on distorted images, dewarping has become a principal preprocessing step. This paper presents a new document dewarping method that removes curl and geometric distortion of modern and historical documents. Finally, the proposed method is evaluated and compared to the existing Computer Vision based method. Most of the traditional dewarping algorithms are created based on the text line feature extraction and segmentation. However, textual content extraction and segmentation can be sophisticated. Hence, the new technique is proposed, which doesn’t need any complicated methods to process the text lines. The proposed method is based on Deep Learning and it can be applied on all type of text documents and also documents with images and graphics. Moreover, there is no preprocessing required to apply this method on warped images. In the proposed system, the document distortion problem is treated as an image-to-image translation. The new method is implemented using a very powerful pix2pixhd network by utilizing Conditional Generative Adversarial Networks (CGAN). The network is trained on UW3 dataset by supplying distorted document as an input and cleaned image as the target. The generated images from the proposed method are cleanly dewarped and they are of high-resolution. Furthermore, these images can be used to perform OCR.