The conversion of scanned documents to digital forms is performed using an Optical Character Recognition (OCR) software. This work focuses on improving the quality of scanned documents in order to improve the OCR output. We create an end-to-end document enhancement pipeline which takes in a set of noisy documents and produces clean ones. Deep neural network based denoising auto-encoders are trained to improve the OCR quality. We train a blind model that works on different noise levels of scanned text documents. Results are shown for blurring and watermark noise removal from noisy scanned documents. Scanned documents are stored as images and need to be processed by an Optical Character Recognition (OCR) software to extract the text contents into a digital format such as an ASCII text file. This is an active research area and there are many tools in the market that process a scanned document and extract the content in a digital format. The success with extraction of digital output is heavily dependent on the quality of the scanned document. In practice, however, there is some noise associated with the scanned document. Typical noises seen in scanned documents are blurring, watermarks, fading, and salt & pepper. With the rise of deep learning adoption in computer vision tasks, there are many neural network models available for image denoising and restoration [1] . However, most of the literature focuses on pictures (e.g., images from natural scenes) but not text documents, and the techniques used are not directly applicable due to very different nature of text document images. The designed REDNET was successfully tested on deblurring document images with various levels of intensity as well as removing both gray-level and color watermarks from text image documents. Currently, research on designing a unified network that can remove all noise types from text documents is ongoing.
We designed and tested a REDNET (ResNet Encoder-Decoder) with 8 skip connections to remove noise from documents, including blurring and watermarks, resulting in a high performance deep network for document image cleanup. 