The efficiency of paperless processes has been steadily increasing over the past years. An aspect of this progress is a significant improvement in the methods of converting paper documents into the electronic form using scanning for data capturing. According to a survey by the Association for Information and Image Management (AIIM), organizations using scanning and capture have improved the speed of response to customers, suppliers, citizens or staff by six times or more, while 29% of the survey respondents have seen a 10× or even better improvements. Individual productivity has also shown a rapid growth with the proliferation of paperless solutions. In the same AIIM survey, 57% of users reported a payback from their scanning and document capture investments within 18-months or less, while 42% of users reported a payback period of 12 months or less. In spite of obvious productivity gains, an overall progress of the paperless society has been slow: over 90% of documents and over 50% of invoices are still created and delivered in the paper form.
Mobile devices, such as smartphones and tablets, have demonstrated a mixed effect over the paperless lifestyle: on the one hand, they offer an alternative to paper-based documents; on the other hand, they are becoming powerful productivity devices with advanced authoring capabilities and therefore increase the overall document volume. Simultaneously, these devices are increasingly acquiring convenient mobile printing capabilities and hence boosting the volume of paper documents. Thus, according to an IDC study, a demand for mobile printing products is expected to increase by 72% from 2010 to 2015. Based on these data and econometric models, many industry experts don't expect paper document volumes to decline rapidly within the next 10 years.
Therefore, under the present conditions, efficient electronic capturing of paper-based documents may remain the prevalent method of developing the paperless lifestyle for years to come.
In addition to personal, mid-range and high-end scanners, smartphones with cameras, supplemented with adequate image processing software, represent a new fast growing segment of the image capturing market. As smartphones are turned into mass market image capturing devices and as scanners acquire batch processing capabilities for heterogeneous documents with different size, paper thickness and condition, two trends in image capturing are becoming prevalent:                1. An overall volume of captured images significantly increases.        2. An average quality of captured images significantly declines. Lighting and printing conditions, backgrounds, paper document wear-and-tear, delivery characteristics are all contributing to such deterioration of image quality.        
One consequence of sub-standard quality of captured images is a noticeably lower recognition accuracy of conventional Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR) methods for transcribing machine printed and handwritten texts from images. Some contemporary systems for robust image processing, such as the Evernote service and software created by the Evernote Corporation of Redwood City, Calif., take into account this challenge and adopt a search-based approach to image recognition: instead of attempting to immediately extract the text from an image, risking multiple recognition errors, such systems may detect word candidates in the image, separating text from the background, recognize the detected word candidates using known OCR, ICR and NHR (Natural Handwriting Recognition) techniques and retain multiple answer options for each word to build search indexes, or recognition contexts, based on such multi-variant text arrays.
Due to high volumes of scanned, photographed, clipped and otherwise captured images entering content management systems, initial identification and categorization of the captured images presents major challenges; generic image names assigned by capturing devices may be illegible, so image attribution and classification may require a significant amount of manual work.
Accordingly, it becomes increasingly important for the progress of the paperless lifestyle to improve identification and categorization of captured document images in contemporary content management systems.