The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing. (30th November 2019)

• Record Type: Journal Article
• Authors: Castro-Bleda, M J; España-Boquera, S; Pastor-Pellicer, J; Zamora-Martínez, F
• Is Part Of: Computer journal. Volume 63:Number 11(2020)
• Page Start: 1658
• Page End: 1667
• Publication Date: 2019-11-30
• Subjects: optical character recognition -- image processing -- binarization -- denoising -- super resolution -- machine learning -- neural networks -- deep learning; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxz098
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: This paper presents the 'NoisyOffice' database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.