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The aim of this paper is to experimentally evaluate the effectiveness of the state-of-the-art printed Arabic text recognition systems to determine open areas for future improvements. In addition, this paper proposes a standard protocol with a set of metrics for measuring the effectiveness of Arabic optical character recognition (OCR) systems to assist researchers in comparing different Arabic OCR approaches.

This paper describes an experiment to automatically evaluate four well-known Arabic OCR systems using a set of performance metrics. The evaluation experiment is conducted on a publicly available printed Arabic dataset comprising 240 text images with a variety of resolution levels, font types, font styles and font sizes.

The experimental results show that the field of character recognition for printed Arabic still requires further research to reach an efficient text recognition method for Arabic script.

To the best of the authors’ knowledge, this is the first work that provides a comprehensive automated evaluation of Arabic OCR systems with respect to the characteristics of Arabic script and, in addition, proposes an evaluation methodology that can be used as a benchmark by researchers and therefore will contribute significantly to the enhancement of the field of Arabic script recognition.

The purpose of this paper is to describe a speech and character combined recognition engine (SCCRE) developed for working on personal digital assistants (PDAs) or on mobile devices. Also, the architecture of a distributed recognition system for providing a more convenient user interface is discussed.

In SCCRE, feature extraction for speech and for character is carried out separately, but the recognition is performed in an engine. The client recognition engine essentially employs a continuous hidden Markov model (CHMM) structure and this CHMM structure consists of variable parameter topology in order to minimize the number of model parameters and to reduce recognition time. This model also adopts the proposed successive state and mixture splitting (SSMS) method for generating context independent model. SSMS optimizes the number of mixtures through splitting in mixture domain and the number of states through splitting in time domain.

The recognition results show that the developed engine can reduce the total number of Gaussian up to 40 per cent compared with the fixed parameter models at the same recognition performance when applied to speech recognition for mobile devices. It shows that SSMS can reduce the size of memory for models to 65 per cent and that for processing to 82 per cent. Moreover, the recognition time decreases 17 per cent with the SMS model while maintaining the recognition rate.

The proposed system will be very useful for many on‐line multimodal interfaces such as PDAs and mobile applications.

The development and Implementation of a character recognition system based on machine vision technology is discussed.

Digital humanities research platform for biographies of Malaysia personalities (DHRP-BMP) was collaboratively developed by the Research Center for Chinese Cultural Subjectivity in Taiwan, the Federation of Heng Ann Association Malaysia, and the Malaysian Chinese Research Center of Universiti Malaya in this study. Using The Biographies of Malaysia Henghua Personalities as the main archival sources, DHRP-BMP adopted the Omeka S, which is a next-generation Web publishing platform for institutions interested in connecting digital cultural heritage collections with other resources online, as the basic development system of the platform, to develop the functions of close reading and distant reading both combined together as the foundation of its digital humanities tools.

The results of the first-stage development are introduced in this study, and a case study of qualitative analysis is provided to describe the research process by a humanist scholar who used DHRP-BMP to discover the character relationships and contexts hidden in The Biographies of Malaysia Henghua Personalities.

Close reading provided by DHRP-BMP was able to support humanities scholars on comprehending full text contents through a user-friendly reading interface while distant reading developed in DHRP-BMP could assist humanities scholars on interpreting texts from a rather macro perspective through text analysis, with the functions such as keyword search, geographic information and social networks analysis for humanities scholars to master on the character relationships and geographic distribution from personality biographies, thus accelerating their text interpretation efficiency and uncovering the hidden context.

At present, a digital humanities research platform with real-time characters’ relationships analysis tool that can automatically generate visualized character relationship graphs based on Chinese named entity recognition (CNER) and character relationship identification technologies to effectively assist humanities scholars in interpreting characters’ relationships for digital humanities research is still lacking so far. This study thus presents the DHRP-BMP that offers the key features that can automatically identify characters’ names and characters’ relationships from personality biographies and provide a user-friendly visualization interface of characters’ relationships for supporting digital humanities research, so that humanities scholars could more efficiently and accurately explore characters’ relationships from the analyzed texts to explore complicated characters’ relationships and find out useful research findings.

Optical character recognition (OCR) is a vital tool for the food and pharmaceutical industries, allowing them to inspect for correct labelling and thereby conforming to good manufacturing practices (GMP).

The purpose of this paper is to present a novel image sensor technology for raised characters based on line structured‐light. It can convert raised character's three‐dimensional (3D) features into image's grayscale levels.

The measurement principle and mathematical model are described. An experimental device is established and system parameters are calibrated. A grayscale conversion algorithm is proposed to convert the distortion of laser stripe to the grayscale intensity of image. The article also introduces a four‐factor method to assess the image quality of characters.

Experimental results show that the method can get high‐contrast images of raised characters that are conventionally low‐contrast with the background. Besides, the method does not need complicated calibration and mass computation, which makes the system structure simple and increases the speed of image acquisition.

The paper presents a novel image acquisition method for raised characters.

Optical character recognition (OCR) technology can be employed to produce an ASCII‐text database for mounting on computer systems. Current technologies and principles of scanning and OCR are discussed. A prototypical “local” project—the creation of a full‐text database of dissertations done at George Mason University—has been undertaken by the Fenwick Library at that institution. Problems encountered with current scanning and OCR technologies are illustrated and discussed, as well as techniques and “filter” programs developed to streamline the scanning and OCR conversion process.

This paper is concerned with the detection of “features” of visual patterns, in particular with a view to the construction of a reading automaton. The features can be divided into micro‐features and macro‐features. It is much more difficult to detect a macro‐feature than a micro‐feature of a pattern. For the detection of at least a large class of macro‐features interative processes seem to be the most appropriate, since by these processes the detection of a macro‐feature is reduced to the repeated detection of a micro‐feature, while further a given micro‐feature can be displaced, by such a process, over virtually the whole screen on which the image is projected. Two examples of such processes are discussed.

This paper analyses the current state of Arabisation of automated library systems. Problems of Arabic language handling and limitations of Romanisation are described. The areas of character encoding, standards for coding and bibliographic records, character recognition and lexical analysis are synthesised. The current state of Arabisation in commercial library systems is reviewed and areas for further research identified.

The very high processing speed of its Linear Array Processor has enabled British Robotic Systems Ltd to win a contract for six character reading systems in a major automation project involving reading of postcodes of poor print quality on DHSS pension books