On-line signature verification involves the use of many different features or domains. The most stable domains for a signer are analysed in this paper. For this purpose, stable domains are calculated with the weighted Direct Matching Points (ωDMP), which is a relaxed version of the classical DMP technique. In addition to the direct coupling, ωDMP also considers the information contained in the 1:N couplings from the Dynamic Time Warping algorithm. Using the ωDMP technique, state-of-the-art verification results are obtained, showing the capacity to outperform previous DMP techniques to calculate the local stability model of signers.

This paper presents an experimental analysis on the use of semi-supervised learning in the handwritten digit recognition field. More specifically, two new feedback-based techniques for retraining individual classifiers in a multi-expert scenario are discussed. These new methods analyze the final decision provided by the multi-expert system so that sample classified with a confidence greater than a specific threshold is used to update the system itself. Experimental results carried out on the CEDAR (handwritten digits) database are presented. In particular, error rate, similarity index and a new correlation score among them are considered in order to evaluate the best retraining rule. For the experimental evaluation, an SVM classifier and five different combination techniques at abstract and measurement level have been used. Finally, the results show that iterating the feedback process, on different multi-expert systems built with the five combination techniques, one retraining rule is winning over the other respect to the best correlation score.

This paper presents a new approach for zoning design based on a class-adaptive technique in which the optimal zoning method is defined for each class. For this purpose, in the zoning design stage, a multi-objective genetic algorithm was used to determine, for each class, both the optimal number of zones and the optimal zones for the Voronoi-based zoning method. The experimental tests were carried out in the field of handwritten digit and character recognition. The results show that the new class-adaptive zoning methods proposed in this paper are superior to the set-adaptive methods presented in the literature.

In the networked society, in which a multitude of different devices can be used for signature acquisition, specific research is still needed to determine the extent to which features of an input signature depend on the characteristics of the signature acquisition process. In this paper an experimental investigation is carried out on constrained signatures, which were acquired using writing boxes with different areas and shapes. The paper discusses different behaviour of dynamic and static features with respect to the writing boxes.

The aim of this paper is to explore the properties of a new zoning technique based on Voronoi tessellation for the task of handwritten digit recognition. This technique extracts features according to an optimal zoning distribution, obtained by an evolutionary-strategy based search. Extensive experiments have been conducted on the MNIST dataset to investigate strengths and weakness of the proposed approach. Comparisons with regular square zoning reveal that the presented zoning strategy achieves better results with any type of features. Furthermore, the proposed zoning method, jointly with a suitable choice of features, allows a low complexity classifier to reach excellent performances both in terms of accuracy and speed.

This paper presents a new approach to optimal zoning design. The approach uses a multi-objective genetic algorithm to define, in a unique process, the optimal number of zones of the zoning method along with the optimal zones, defined through Voronoi diagrams. The experimental tests, carried out in the field of handwritten digit recognition, show the superiority of new approach with respect to traditional dynamic approaches for zoning design, based on single-objective optimization techniques.

In pattern recognition tasks it is frequent that new (labeled) data became available as the specific application scenario evolves. When a multi expert system (ME) is adopted, the collective behavior of classifiers can be used to select the most profitable samples in order to update the knowledge base. More specifically a misclassified sample, for a particular classifier, is used to update that classifier only if that sample produces a misclassification by the ensemble of classifiers. This approach is compared to situation in which the entire new dataset is used for learning as well as the case in which specific samples are selected by the individual classifier. Successful results have been obtained by considering the CEDAR (handwritten digit) database, moreover it is also shown how they depend by the specific combination decision schema, as well as by data distribution.

Classifier combination is a powerful strategy to support useful solutions in difficult classification problems. Notwithstanding, the effectiveness of a multi-classifier system strongly depends on the decision fusion strategies. In this field, one of the most significant aspects concerns output normalization,when classifiers decisions are provided at measurement level. This paper presents a new approach for output normalization that uses dynamic time warping (DTW). Some experimental tests have been carried out in the field of handwritten digit recognition. The proposed approach is superior to other output normalization algorithms in the literature.

Multi-classifier approach is a widespread strategy used in many difficult classification problems.Traditionally, in a multi-classifier approach, a classification decision based on the combination of a multitude of classifiers is expected to outperform the decisions of each individual classifier. Therefore, in a multi-classifier systems, the potential of the whole set of classifiers is only exploited at the level of the final decision, in which the contributions of all classifiers is used by combining their individual decisions.This paper shows a feed-back based multi-classifier system in which the multi-classifier approach is used not only for providing the final decision, but also for improving the performance of the individual classifiers, by means of a closed-loop strategy.The experimental tests have been carried out in the field of hand-written numeral recognition. The result demonstrates the effectiveness of the proposed approach and its superiority with respect to traditional approach.

The introduction of a new currency in Europe has changed the way of writing both the courtesy and the legal amount on checks. This paper presents the most important modifications brought on the bank-check processing system in order to solve the related problems also by proposing the software tools that must be utilized. The Computer Aided Software Engineering tools provided by the "Khoros" system are used to support the improvement of the system prototype. A visual programming environment is used to assemble the bankcheck processing system that can be easily modified and extended. The experimental results allow the adjournment of the improved system, as the modifications are introduced.