The prediction of the linguistic origin of surnames is a basic functionality required in the design of high-quality multilanguage speech synthesizers. The assignment of a given string representing a surname to a specific language is typically based on a set of rules which can hardly be written in an explicit form. The approach we propose faces this problem combining a rule-based system with a module based on evidential reasoning and a module based on neural networks. The resulting hybrid system combines the different sources of information, merging both knowledge from experts on linguistics and knowledge automatically acquired using learning from examples. The system has been validated on a large database containing surnames belonging to four different languages, showing its effectiveness for real-world applications.

This paper describes an application to the prediction of the linguistic origin of surnames. The problem can be stated as follows: "Given an input string representing a surname, decide which language the surname belongs to". We present an approach that integrates methodologies from rule-based systems, evidential reasoning, and neural networks. Our hybrid solution maximizes the used information and allows one to deal with aspects of the problem that could have not been solved otherwise. In fact, our predictor exploits both knowledge from experts on languages (by means of a rule-based system) and knowledge automatically acquired by examples (through statistical analysis and a neural network).

Much attention has recently been paid to the recognition of graphical objects, such as company logos and trademarks. Recognizing these objects facilitates the recognition of document classes. Some promising results have been achieved by using autoassociator-based artificial neural networks (AANN) in the presence of homogeneously distributed noise. However, the performance drops significantly when dealing with spot-noisy logos, where strips or blobs produce a partial obstruction of the pictures. We propose a new approach for training AANNs especially conceived for dealing with spot noise. The basic idea is to introduce new metrics for assessing the reproduction error in AANNs. The proposed algorithm, referred to as spot-backpropagation (S-BP), is significantly more robust with respect to spot-noise than classical Euclidean norm-based backpropagation (BP). Our experimental results are based on a database of 88 real logos that are artificially corrupted by spot-noise.

We present a method for the logical labelling of physical rectangles, extracted from invoices, based on a conceptual model which describes, as generally as possible, the invoice universe. This general knowledge is used in the semi automatic construction of a model for each class of invoices. Once the model is constructed, it can be applied to understand an invoice instance, whose class is univocally identified by its logo. This approach is used to design a flexible system which is able to learn, from a nucleus of general knowledge, a monotonic set of specific knowledge for each class of invoices (document models), in terms of physical coordinates for each rectangle and related semantic label.