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Optical Character Recognition (OCR) is the digitisation of printed pages into editable and searchable computer readable texts. OCR software analyses patterns in an electronic image of the page to work out which letters and words are in the document. A major issue is quality. Even 99% accuracy would give 2 errors every 3 lines. One form of quality control is proof reading, but this is expensive. An alternative is Adobe Capture which replaces suspect reads with a ‘bitmap’, a picture of the original.

Since translating is an office activity which, like other office activities, consists primarily of processing text, it is instructive to examine the reasons for automating text production. Most of these reasons will be equally applicable to the production of translated texts. We shall then investigate the current developments in machines and micro‐chip technology which are applicable to translation. These will include voice recognition and response and optical character recognition equipment amongst others.

Outlines some of the areas in machine vision system solution which have seen the most significant advances as a result of technology enhancement and particularly the rapid development in semiconductor technology. Looks at the background of machine vision development and improvements brought about by modern technology, covering advances in vision algorithms, “warp engines” used in combination with an application specific integrated circuit [ASIC], improvements in human interface; and optical character recognition. Concludes that the trend now is for general purpose vision processing systems to provide greater capability, at greater throughput speed, without significantly increasing the cost of the system which means that they are now part of the original process design to inspect critical stages of manufacture.

Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.

The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.

The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.

The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.

An optical character recognition system is helping with the automatic assembly of automotive parts at Rover's Longbridge plant.

The costs of alternative methods of storing data have changed significantly as electronic systems have evolved. Moreover, we expect the average level of costs to continue falling over the next decade as technical change continues. Electronic systems are becoming closer substitutes for traditional ways of storing information in libraries. This issue's column examines the storage capacity of a wide array of representative storage devices in terms of the number of billions of characters or bytes of information—that is, gigabytes of storage—and each system's costs. The cost per gigabyte (GB) of storage varies by several orders of magnitude in ways that have important implications for the evolution of libraries over the next decade.

To meet the needs of the environment and to improve competitiveness through lower costs and greater responsiveness, the paperless office is once again being anticipated. While the concept of the paperless office was subject to derision until just recently due to the large volume of paper currently being produced, the large reduction in the costs of personal computers has made the paperless office viable. More than just a tool, it will redefine our concept of the document and the way we do business. Explains various facets of the paperless office. Electronic mail (e‐mail) will make the generation and transmission of letters and memos more efficient. Business forms will be better utilized through the improved processing offered by computerization and hand writing recognition software. Also addresses how document management makes information more readily accessible through the use of improved indexing.

OCR and COM are relevant to and useful in the computer operation of information systems; both usually involve the use of an outside bureau. This paper describes both techniques, with emphasis on practical points for making the most efficient use of each. Guidelines to the selection of OCR and COM bureaux, to systems design, and to the costs of the techniques are given.

The purpose of this paper is to introduce mobile augmented reality applications for library uses and next generation library services.

Examples are drawn from museum and archives informatics, computer science applied research, and computer vision research as well as original research and development work from the Undergraduate Library at the University of Illinois.

Mobile augmented reality uses include augmenting physical book stacks browsing, library navigation, optical character recognition, facial recognition, and building identification mobile software for compelling library experiences.

The paper suggests uses of mobile augmented reality applications in library settings and models a demonstration prototype interface.

Book sorting system is one of specific application in smart library scenarios, and it now has been widely used in most libraries based on RFID (radio-frequency identification devices) technology. Book identification processing is one of the core parts of a book sorting system, and the efficiency and accuracy of book identification are extremely critical to all libraries. In this paper, the authors propose a new image recognition method to identify books in libraries based on barcode decoding together with deep learning optical character recognition (OCR) and describe its application in library book identification processing.

The identification process relies on recognition of the images or videos of the book cover moving on a conveyor belt. Barcode is printed on or attached to the surface of each book. Deep learning OCR program is applied to improve the accuracy of recognition, especially when the barcode is blurred or faded. The approach the authors proposed is robust with high accuracy and good performance, even though input pictures are not in high resolution and the book covers are not always vertical.

The proposed method with deep learning OCR achieves best accuracy in different vertical, skewed and blurred image conditions.

Methods that the authors proposed need to cooperate and practice in different book sorting machine.

The authors collected more than 500 books from a library. These photos display the cover of more than 100 randomly picked books with backgrounds in different colors, each of which has about five different pictures captured from variety angles. The proposed method combines traditional barcode identification algorithm with the authors’ modification to locate and deskew the image. And deep learning OCR is involved to enhance the accuracy when the barcode is blurred or partly faded. Book sorting system design based on this method will also be introduced.

Experiment demonstrates that the accuracy of the proposed method is high in real-time test and achieves good accuracy even when the barcode is blurred. Deep learning is very effective in analyzing image content, and a corresponding series of methods have been formed in video content understanding, which can be a greater advantage and play a role in the application scene of intelligent library.