Describes a method for classifying document images on the basis of their physical layout. The layout is described by means of a hierarchical description, the modified X-Y tree, that is derived from the classical X-Y tree segmentation algorithm taking into account cuts along lines in addition to cuts along white spaces between blocks. In order to reduce problems due to noise and the skew of the input image, the modified X-Y tree is built on top of regions extracted by a commercial OCR package. The tree is afterwards coded into a fixed-size representation that takes into account occurrences of tree patterns in the tree representing the page. Lastly, this feature vector is fed to an artificial neural network that is trained to classify document images. The system is applied to the classification of documents belonging to digital libraries. Examples of classes taken into account are "title page", "index" and "regular page". Many tests have been carried out on a data set of more than 600 pages from an online digital library. These tests allowed us to conclude that the use of modified X-Y trees is advantageous with respect to the classical X-Y decomposition for this classification task.

In this paper an architecture for understanding documents of a domain that can be grouped into classes is shown. Documents are grouped with respect to the physical structure. The architecture is based on two knowledge descriptions of the domain: one is independent from the classes and one related to the classes. Such knowledge levels are used to understand the documents of the domain. The understanding phase is described in relation with the phases of analysis and classification of such documents.

We describe a top-down approach to the segmentation and representation of documents containing tabular structures. Examples of these documents are invoices and technical papers with tables. The segmentation is based on an extension of X-Y trees, where the regions are split by means of cuts along separators (e.g. lines), in addition to cuts along white spaces. The leaves describe regions containing homogeneous information and cutting separators. Adjacency links among leaves of the tree describe local relationships between corresponding regions.

We describe a flexible form-reader system capable of extracting textual information from accounting documents, like invoices and bills of service companies. In this kind of document, the extraction of some information fields cannot take place without having detected the corresponding instruction fields, which are only constrained to range in given domains. We propose modeling the document's layout by means of attributed relational graphs, which turn out to be very effective for form registration, as well as for performing a focused search for instruction fields. This search is carried out by means of a hybrid model, where proper algorithms, based on morphological operations and connected components, are integrated with connectionist models. Experimental results are given in order to assess the actual performance of the system.

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.

We formally investigate the structural similarities and differences existing between object database models and concept languages establishing a correspondence between the two environments. Object database models deal with two kinds of data: individual objects, which have an identity, and values, which can be basic values or can have complex structures containing both basic values and objects. Concept languages only deal with individual objects. The correspondence points out the different role played by objects and values in both approaches and defines a way of properly mapping database descriptions into concept language descriptions at both a terminological and assertional level. Once the mapping is achieved, object databases can take advantage of both the algorithms and the results concerning their complexity developed in concept languages.

In this paper, we describe a flexible and efficient system for processing forms of a known class. The model is based on attributed relational graphs and the system performs form registration and location of information fields using algorithms based on the hypothesize-and-verify paradigm. A special emphasis has been placed at the low level, where an autoassociator-based connectionist model has exhibited successful results in finding the instruction fields in very noisy forms.

We present a dynamic external hash method that allows retrieval of a record by only one access to mass storage while maintaining a high load factor. The hash function is based on generalized spiral storage. Both primary and overflow records are allocated to the same file, and file expansion depends on being able to allocate every overflow chain to one bucket. An in-core index, built by means of a signature function, discriminates between primary and overflow records and assures one access to storage in the case of either successful or unsuccessful searching. Simulation results confirm the good expected performance.

The aim of this paper is to present the design and implementation of Mimirel, a relational Data Base Management System for microcomputers especially directed to non-specialist end users. The system overcomes architectural limits of microcomputers by means of a physical data organisation which allows space saving and fast inquiring. Furthermore, the user interface has been developed so as to take the needs of typical microcomputer end user into account. The critical operations are system driven in a conversational way and suitable facilities are provided for the inquiring. The Mimirel system has been fully implemented in Pascal, under the CP/M operating system and so it is highly portable.