Ahmad Fuad Rezaur Rahman

Multiple classifier decision combination strategies for character recognition: A review

Abstract.Two research strands, each identifying an area of markedly increasing importance in the current development of pattern analysis technology, underlie the review covered by this paper, and are drawn together to offer both a task-oriented and a fundamentally generic perspective on the discipline of pattern recognition. The first of these is the concept of decision fusion for high-performance pattern recognition, where (often very diverse) classification technologies, each providing complementary sources of information about class membership, can be integrated to provide more accurate, robust and reliable classification decisions. The second is the rapid expansion in technology for the automated analysis of (especially) handwritten data for OCR applications including document and form processing, pen-based computing, forensic analysis, biometrics and security, and many other areas, especially those which seek to provide online or offline processing of data which is available in a human-oriented medium. Classifier combination/multiple expert processing has a long history, but the sheer volume and diversity of possible strategies now available suggest that it is timely to consider a structured review of the field. Handwritten character processing provides an ideal context for such a review, both allowing engagement with a problem area which lends itself ideally to the performance enhancements offered by multi-classifier configurations, but also allowing a clearer focus to what otherwise, because of the unlimited application horizons, would be a task of unmanageable proportions. Hence, this paper explicitly reviews the field of multiple classifier decision combination strategies for character recognition, from some of its early roots to the present day. In order to give structure and a sense of direction to the review, a new taxonomy for categorising approaches is defined and explored, and this both imposes a discipline on the presentation of the material available and helps to clarify the mechanisms by which multi-classifier configurations deliver performance enhancements. The review incorporates a discussion both of processing structures themselves and a range of important related topics which are essential to maximise an understanding of the potential of such structures. Most importantly, the paper illustrates explicitly how the principles underlying the application of multi-classifier approaches to character recognition can easily generalise to a wide variety of different task domains.

Recognition of handwritten Bengali characters: a novel multistage approach

A multistage scheme for the recognition of handwritten Bengali characters is introduced. An analysis of the Bengali character set has been carried out to isolate specific high-level features that can help in forming smaller sub-groups within the character set. This analysis demonstrates how detection of these various high-level features might help formulate successful multistage OCR design. A multiple expert decision combination hierarchy has been exploited to achieve higher performance from the proposed multi-stage framework.

Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variations

In recent years, strategies based on combination of multiple classifiers have created great interest in the character recognition research community. A huge number of complex and sophisticated decision combination strategies have been explored by researchers. However, it has been realized recently that the comparatively simple Majority Voting System and its variations can achieve very robust and often comparable, if not better, performance than many of these complex systems. In this paper, a review of various Majority Voting Systems and their variations are discussed, and a comparative study of some of these methods is presented for a typical character recognition task.

An evaluation of multi-expert configurations for the recognition of handwritten numerals

In recent years, combination of multiple experts has become a major area of interest in designing practical and robust handwritten character recognition systems. Instead of building a single sophisticated and complicated classifier which is capable to handling all the various types of variations that are present in the handwritten character set, it has proved more prudent to apply relatively simpler classifiers (experts) by formulating ways of combining their individual decisions in order to generate robust and confident decisions. This paper presents a new class of decision combination approaches and compares the effectiveness of these approaches in successfully combining decisions by multiple experts in the specific application of handwritten numeral recognition. Although the proposed approaches have been applied to a specific task of handwritten numeral recognition, the underlying concepts are completely generalised and should be applicable to a very broad task domain.

Serial Combination of Multiple Experts: A Unified Evaluation

Abstract: Multiple expert decision combination has received much attention in recent years. This is a multi-disciplinary branch of pattern recognition which has extensive applications in numerous fields including robotic vision, artificial intelligence, document processing, office automation, human-computer interfaces, data acquisition, storage and retrieval, etc. In recent years, this application area has been extended to forensic science, including the identification of individuals using measures depending on biometrics, security and other applications. In this paper, a generalised multi-expert multi-level decision combination strategy, the serial combination approach, has been investigated from the dual viewpoints of theoretical analysis and practical implementation. Different researchers have implicitly utilised various approaches based on this concept over the years in a wide spectrum of application domains, but a comprehensive, coherent and generalised presentation of this approach from both theoretical and implementation viewpoints has not been attempted. While presenting here a unified framework for serial multiple expert decision combination, it is shown that many multi-expert approaches reported in the literature can be easily represented within the proposed framework. Detailed theoretical and practical discussions of the various performance results with these combinations, analysis of the internal processing of this approach, a case study for testing the theoretical framework, issues relating to processing overheads associated with the implementation of this approach, general comments on its applicability to various task domains and the generality of the approach in terms of reevaluating previous research have also been incorporated.

A new hybrid approach in combining multiple experts to recognise handwritten numerals

Abstract Hand written numeral recognition is an area of pattern recognition that has applications in numerous fields including automated postal sorting, automatic bank cheque processing, hand written document analysis and so on. Recently, the potential advantages of using multiple experts in a unified structure have been demonstrated in addressing the problem of classification of hand written numerals. The motivation behind this paper is to implement a new approach to the solution of the problem of combining the decisions made by multiple experts, by making use of the restrictive and repetitive nature of the numeral structures and combining the a priori knowledge of the expected numeral classes that are to be processed and recognised with that derived from the training samples.

A New Multi-Expert Decision Combination Algorithm and its Application to the Detection of Circumscribed Masses in Digital Mammograms

A new multiple expert fusion algorithm is introduced, designated the “augmented behaviour-knowledge space method”. Most existing multiple expert classification methods rely on a large training dataset in order to be properly utilised. The proposed method effectively overcomes this problem as it exploits the confidence levels of the decisions of each classifier. It will be shown that this new approach is advantageous when small datasets are available, and this is illustrated in its application to the detection of circumscribed masses in digital mammograms, with very encouraging results.

Automatic summarization of Web content to smaller display devices

Web documents usually have complicated layouts and the overall information content can be huge. All these documents are designed for viewing in large screen devices, such as a computer monitor. In recent times, a large number of small screen portable devices, such as personal digital assistants (PDA) and cellular phones, have been made available for mobile browsing. Viewing a Web page originally written for large screen devices using these very small screen devices can be extremely cumbersome. This paper discusses this issue of small viewing form factor of electronics devices from the perspective of Web browsing and proposes an approach to automatically summarize and transform Web documents into a meaningful, readable and above all, browsable format.

Introducing new multiple expert decision combination topologies: a case study using recognition of handwritten characters

A new topology for classifying decision combinations of multiple experts in the framework of a multiple expert character recognition platform is introduced. It is demonstrated that many existing multiple expert configurations for character recognition can be categorised by using this method of defining classification strategies. It is also demonstrated that the design of multiple expert character recognition configurations can be streamlined by classifying these structures in terms of how the channels used for carrying information among different experts are interconnected irrespective of the algorithms used by cooperating experts and by the final decision combination expert. Case studies of actual multiple expert character recognition configurations have been investigated and it is shown how they can be categorised with respect to the decision combination topologies introduced in the paper.

Multiple expert classification: a new methodology for parallel decision fusion

Abstract. A new parallel hybrid decision fusion methodology is proposed. It is demonstrated that existing parallel multiple expert decision combination approaches can be divided into two broad categories based on the implicit decision emphasis implemented. The first category consists of methods implementing computationally intensive decision frameworks incorporating a priori information about the target task domain and the reliability of the participating experts, while the second category encompasses approaches implementing group consensus without assigning any importance to the reliability of the experts and ignoring other contextual information. The methodology proposed in this paper is a hybridisation of these two approaches and has shown significant performance enhancements in terms of higher overall recognition rates along with lower substitution rates. Detailed analysis using two different databases supports this claim.