A. Salzo

Automatic bankcheck processing : A new engineered system

A new bankcheck processing system is presented in this paper. A full exploitation of the contextual knowledge, together with a multi-expert approach, have been used both to analyze the complex shape of handwritten text and to design the system. Several processing modules have been integrated in the system. Some of the most relevant are those for data acquisition, preprocessing, machine-printed numeral recognition, layout analysis, courtesy amount recognition, legal amount recognition, amount validation, and signature verification. Some combination techniques have also been used in the system. Reuse and maintenance of the system were two of the main goals of the designing process and the Khoros software tool was used for this purpose.

A multi-expert signature verification system for bankcheck processing

In this paper a multi-expert signature verification system is presented. The system has been specifically designed for applications in the field of bankcheck processing. For this purpose, it combines three different algorithms for signature verification. A wholistic approach is used in the first algorithm, a component-oriented approach is used in the second and third algorithms. The second algorithm is based on a structure-based procedure, the third algorithm uses a highly-adaptive neural network. The three algorithms are combined in the multi-expert system by a voting strategy.

A Multi-expert System for Dynamic Signature Verification

This paper presents a multi-expert system for dynamic signature verification. The system combines three experts whose complementar behaviour is achieved by using both different features and verification strategies. The first expert uses shape-based features and performs signature verification by a wholistic analysis. The second and third expert uses speedbased features and performs signature verification by a regional analysis. Finally, the verification responses provided by the three experts are combined by majority voting.

Zoning Design for Handwritten Numeral Recognition

This paper presents a new approach for zoning design. The approach is based on a techinque which detects the most discriminant image regions by the analysis of feature distributions, and obtains the zoning by an iterative zone-growing process. An application to handwritten numeral recognition is also reported showing the effectiveness of the proposed approach.

Evaluation of combination methods

This paper presents a new approach to evaluate the performances of combination methods, which takes into account both the recognition rates of the experts combined and the correlation among them. At the purpose, a suitable estimator of correlation is defined. Two combination methods have been considered: majority vote and Dempster Shafer. A statistical test, based on the analysis of variance, has also been used to infer some interesting considerations on the behaviour of combination methods. The paper shows how the proposed approach allows the selection of the best combination method for each set of experts.

On the combination of

Abstract.This paper presents a framework for the analysis of similarity among abstract-level classifiers and proposes a methodology for the evaluation of combination methods. In this paper, each abstract-level classifier is considered as a random variable, and sets of classifiers with different degrees of similarity are systematically simulated, combined, and studied. It is shown to what extent the performance of each combination method depends on the degree of similarity among classifiers and the conditions under which each combination method outperforms the others. Experimental tests have been carried out on simulated and real data sets. The results confirm the validity of the proposed methodology for the analysis of combination methods and its usefulness for multiclassifier system design.

{\it abstract-level}

In this paper a new evaluation method for expert combination is presented. It takes into account the correlation among experts, their number and their recognition rate. An extended investigation on Majority Vote, Bayesian, Behaviour Knowledge Space and Dempster-Shafer method for abstract-level classifiers is presented. The two-way analysis of variance test and the Scheffe post-hoc comparison have been used to investigate on the factors that influence the recognition rate of the multi-expert system and to collect useful information for the multi-expert system designing.

classifiers

This paper presents the State of the Art on handwriting recognition and points out some major future trends of scientific research in this field. Some of the most relevant results presented in the series of Workshops on Frontiers in Handwriting Recognition and Conferences on Document Analysis and Recognition are also discussed and an extensive bibliography of selected papers is reported.

A New Evaluation Method for Expert Combination in Multi-expert System Designing

This paper addresses the problem of dynamic configuration of multi classifier systems. For this purpose, the performance of combination methods for abstract-level classifiers is predicted, under different working conditions, and sets of rules are discovered and used for dynamic configuration of multiclassifier systems. The experimental tests have been carried out in the field of hand-written numeral recognition. The result demonstrates the validity of the proposed approach.

Handwriting Recognition: State of the Art and Future Trends

Complementarity among classifiers is a crucial aspect in classifier combination. A combined classifier is significantly superior to the individual classifiers only if they strongly complement each other. In this paper a complementarity-based analysis of sets of classifier is proposed for investigating the behaviour of multi-classifier systems, as new classifiers are added to the set. The experimental results confirm the theoretical evidence and allow the prediction of the performance of a multi-classifier system, as the number of classifiers increases.