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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.

The purpose of this paper is to evaluate the performance of table type radio frequency identification (RFID) reader and verify the effect of the use of a parasitic element on the table type RFID reader to increase the communication performance and improve the automatic identification of books in the library.

In this study, the authors observe the magnetic field at each point on the reader by using a small probe antenna and evaluate the reading performance of table type RFID reader at each point on the reader by using a RFID tag. Furthermore, to increase the communication performance, they add a parasitic element on the table type RFID reader and evaluate its usefulness.

The power distribution on the table type RFID reader and the communication performance of the table type REID reader were clearly shown. From the experiments of the magnetic field observation, when the coil surface of a tag is put in the parallel with the antenna plane of the reader, the tag can obtain electric power from the reader most effectively. Furthermore, by using a loop coil between the reader and the tag as a parasitic element, the points where communication is enabled with tag increased as the number of turns of coil increased.

In this research work, the author clearly showed the table type RFID reader’s performance by experiment. Moreover, to increase the communication performance, the author proposed to add a parasitic element to the table type RFID leader.

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.

Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.

In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.

The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.

The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

Allen‐Bradley recently announced a new addition to the company's CVIM vision input module offering. The Allen‐Bradley optical character recognition package, OCR‐PAK, allows the CVIM module to read character strings within an image for product identification.

The current predominant delivery format resulting from digitization is PDF, which is not appropriate for the blind, partially sighted and people who read on mobile devices. To meet the needs of both communities, as well as broader ones, alternative file formats are required. With the findings of the eBooks-On-Demand-Network Opening Publications for European Netizens project research, this study aims to improve access to digitized content for these communities.

In 2022, the authors conducted research on the digitization experiences of 13 EODOPEN partners at their organizations. The authors distributed the same sample of scans in English with different characteristics, and in accordance with Web content accessibility guidelines, the authors created 24 criteria to analyze their digitization workflows, output formats and optical character recognition (OCR) quality.

In this contribution, the authors present the results of a trial implementation among EODOPEN partners regarding their digitization workflows, used delivery file formats and the resulting quality of OCR results, depending on the type of digitization output file format. It was shown that partners using the OCR tool ABBYY FineReader Professional and producing scanning outputs in tagged PDF and PDF/UA formats achieved better results according to set criteria.

The trial implementations were limited to 13 project partners’ organizations only.

This research paper can be a valuable contribution to the field of massive digitization practices, particularly in terms of improving the accessibility of the output delivery file formats.

The purpose of this paper is to address the most urgent challenges that libraries face in the mass digitization of historical printed text: the unsatisfactory result of the conversion of scanned images to full featured electronic text by means of automated optical character recognition (OCR); the historical language barrier around 1850, caused by inadequacy of most existing lexica for historical language for OCR or post‐correction and a lack of institutional knowledge and expertise in libraries, museums and archives.

In the EC‐funded project IMPACT (Improving Access to Text), seven libraries, six research institutes and two private sector companies across Europe work together to address the challenges by the development of OCR software and technologies which exceed the accurateness of current state‐of‐the‐art software significantly. The IMPACT solutions focus on the entire process of recognition after the document leaves the scanner: Image processing, OCR processing (including use of dictionaries), OCR correction and Document formatting. IMPACT will also build capacity in mass digitization by sharing best practice and expertise with the cultural heritage communities in Europe.

Technical results will include toolkits for image enhancement and segmentation, an adaptive OCR engine and several prototypes of experimental OCR engines, computational lexica and several post‐correction modules including a web based collaborative correction system and a parser for structural metadata. Strategic tools include several decision support tools, guidelines, a web site with demonstrator platform, a training programme and ultimately, a sustainable Centre of Competence for mass digitization in Europe.

The IMPACT solutions will allow for the first time to transform large amounts of digitized historical texts into electronic text with a minimum of manual interference and a significantly improved accessibility for the user.

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

The idea of optical character recognition (OCR), in other words the “reading” of documents by other than human means, arose as a practical proposition during the Second World War. Wartime experience of using computers in the United States had revealed the contrasts in speeds between the transcription of documents to be processed (at that time the punching of cards or tape by operatives working from original documents) and the central processing within the computer itself. Visual output was also slower than central processing but was much speeded up by the introduction of line printers and later of xerography. This “paired” case study, part of a project sponsored by the Science Research Council to examine patterns of success and failure in industrial innovation, is confined to two attempts to innovate in the field of OCR. There were others, one or two of which were contemporary, most of which have followed, have a much more recent history and may be thought to have overtaken, in terms of market penetration, the innovation here designated a commercial success. The point of this study when it was undertaken was to extract data about the two innovations that would be suitable for general analysis by a computer programme designed to search out significant groups of explanatory factors so that the characteristics associated with innovative success might be recognised as typical within an industry, or perhaps generally. This study belongs to one of two groups, the instrument industry, the other group investigated being chemical manufacturing.