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Learning Chinese is unquestionably very important and popular worldwide with the fast economic growth of China. To most foreigners and also local students, one of the major challenges in learning Chinese is to write Chinese characters in correct stroke sequences that are considered as significant in the Chinese culture. However, due to the potentially complicated structures of Chinese characters together with their stroke sequences, there are very few character recognition techniques that can effectively tackle the involved training task in an efficient and flexible manner. The purpose of this paper is to propose an intelligent and flexible e‐learning software based on learning objects to facilitate the learning of writing Chinese characters in correct stroke sequences.

The paper adopts an incremental approach in designing the overall system architecture to emphasize on extendibility of the system. The basic features of the system including the evolution and pronunciation of each Chinese character can be embedded as a part of the learning object metadata to enhance students' understanding of Chinese characters. To demonstrate the feasibility of this proposal, a prototype of the proposed e‐learning software was built on smartphones such that students can learn anytime and anywhere.

From the empirical evaluation of the e‐learning prototype for learning to write correct Chinese characters on mobile devices, it was found that foreign students can learn and practise the writing more effectively anytime and anywhere on their mobile devices after classes. Some initial positive feedback was collected. Furthermore, a more careful and thorough evaluation is planned to be conducted in relevant courses for foreign students in the upcoming Fall semester.

This proposal represents the first attempt to reduce the complexity while increasing the extendibility of the e‐learning software to learn Chinese through learning objects running on smartphones or mobile devices in general. More importantly, it opens up numerous opportunities for further investigations including possible integrations with other existing Chinese e‐learning systems.

The authors investigate optical character recognition (OCR) technology and discuss its implementation in the context of digitisation of archival materials.

The typewritten transcripts of the Croatian Writers' Society from the mid-60s of the 20th century are used as the test data. The optimal digitisation setup is investigated in order to obtain the best OCR results. This was done by using the sample of 123 pages digitised at different resolution settings and binarisation levels.

A series of tests showed that different settings produce significantly different results. The best OCR accuracy achieved at the test sample of the typewritten documents was 95.02%. The results show that the resolution is significantly more important than binarisation pre-processing procedure for achieving better OCR results.

Based on the research results, the authors give recommendations for achieving optimal digitisation process setup with the aim of increasing the quality of OCR results. Finally, the authors put the research results in the context of digitisation of cultural heritage in general and discuss further investigation possibilities.

In artificial intelligence, the optical character recognition (OCR) is an active research area based on famous applications such as automation and transformation of printed documents into machine-readable text document. The major purpose of OCR in academia and banks is to achieve a significant performance to save storage space.

A novel technique is proposed for automated OCR based on multi-properties features fusion and selection. The features are fused using serially formulation and output passed to partial least square (PLS) based selection method. The selection is done based on the entropy fitness function. The final features are classified by an ensemble classifier.

The presented method was extensively tested on two datasets such as the authors proposed and Chars74k benchmark and achieved an accuracy of 91.2 and 99.9%. Comparing the results with existing techniques, it is found that the proposed method gives improved performance.

The technique presented in this work will help for license plate recognition and text conversion from a printed document to machine-readable.

For many years the philosophy of machines reading data from paper into computers, known as Optical Character Recognition or OCR, has been regarded by the unenlightened as a dream which will be wonderful when it happens. OCR has, unfortunately, been an insufficiently publicized reality for 25 years.

The Fred Meyer Charitable Trust, Division of Library and Information Resources for the Northwest, has funded five research projects that will demonstrate the potential of various techniques and new technologies to facilitate communications and resource sharing in the Northwest. The experience and information derived from these projects will be of value to all libraries and information centers, not just those conducting the research. The techniques and technologies being evaluated include: simultaneous remote searching, which uses inexpensive terminals and modems; a mini‐computer‐based union list and resource sharing network (INFONET); networks using facsimile machines; networks that transmit documents that have been optically scanned into bit‐map image files; and use of optical character recognition equipment to capture ASCII machine‐readable information that can be broadcast by television stations to user‐sites. Contributors of reports are: Verl Anderson, Linda Brander, Millard F. Johnson, Jr., Bruce Morton, and Steve Smith. Summary observations are provided by Joseph R. Matthews.

The promoters of PC Expo claimed that 50,000 attendees would crowd the Jacob K. Javits Center in New York City for the September 1–3 show. Hordes of “volume buyers” (corporate computer honchos) and “computer resellers” (computer dealers of various stripes) were to fill the aisles.

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

This study helps librarians, scientists and funders understand smart library trends.

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

It is difficult to argue against the fact that research has focussed on artificial intelligence (AI) and robotisation over the past few decades. Additionally, during the past several years, it has taken off and is now extensively used in numerous businesses across various industries. Most of the time, AI has been associated with some industrial sector process automation. Still, recently, the authors have noticed more positive technology uses, especially in the financial services industry. Due to several factors, the financial sector needs to adopt AI and recognise its potential. The industry has historically been concerned about unpredictability, legislation, stronger cybersecurity, technological limitations and disruption of established lucrative operations.

Never before has there been more discussion about AI due to the advantages it provides to businesses that are providing financial services. That may explain why this change is referred to as the fourth industrial revolution. Both positively and negatively, it is quite disruptive. The effectiveness, accuracy and cost-effectiveness of solutions greatly increase. However, immense power also entails great responsibility.

Precautions and security are more crucial than ever for businesses since the financial sector is changing significantly and quickly. The various benefits and drawbacks of this technology are yet unknown to humans. Although AI was first shown to us in the 1950s, it has recently gained new prominence as processing power, and the available quantity of data has increased dramatically.

The purpose of this paper is to define and stimulate interest in a potential new specialty within the information science field.

Sources on digital forensics and digital archeology are discussed, and the topic is examined critically from a librarian perspective. The author examines the possibility of an information science specialty pursuing the reconstruction of “digital palimpsests”, where data that later becomes historically significant has been deleted or partially overwritten on digital media.

The author identifies at least one key incident (the NASA moon landing tapes) where this potential field has already started to be defined. Examination of the literature indicates that emphasis in data recovery to this point has centered on the needs of law enforcement and disaster recovery rather than on the considerations of manuscript preservation, recovery, and curation. The author emphasizes the need for librarians to bring together the skills of multiple fields, especially that of information technology, in order to shape the tools needed to take the lead in “digital palimpsest” recovery.

The author asserts that the recovery of “digital palimpsests” will become important as digital archives age and society's position on what has historical value inevitably shifts. The author further asserts that members of the information science field must actively work to take ownership of the field before it is subsumed by information technology or another discipline less equipped to manage its nebulous considerations effectively.