In the last years the interest in e-book readers is significantly growing. Two main document formats are supported by most devices: PDF and ePub. The PDF format is widely used to share documents allowing a cross-platform readability. However, it is not ideal for a comfortable reading on small screens. On the opposite, the ePub format is re-flowable and it is well suited for e-book readers. In this paper we describe a system for the conversion of PDF books to the ePub format aiming at inverting the text formatting made during the pagination. To this purpose, layout analysis techniques are performed to identify the book's table of contents and the main functional regions such as chapters, paragraphs, and notes.

In this paper, the Earth Mover's Distance (EMD) is used as a similarity measure in the mathematical symbol retrieval task. The approach is based on the Bag-of-Visual-Words model. In our case the features extracted from each symbol are clustered by means of Self-Organizing Maps (SOM) and then occurrences of features in the clusters are accumulated in a vector of visual words. The comparison between the latter vectors is performed with the EMD which naturally allows to incorporate the topological organization of SOM clusters in the distance computation. The proposed approach is experimentally tested in a mathematical symbol retrieval task and compared with the cosine similarity and with some variants that have been recently proposed.

We describe one tool for Table of Content (ToC) identification and recognition from PDF books. This task is part of ongoing research on the development of tools for the semi-automatic conversion of PDF documents in the Epub format that can be read on several E-book devices. Among various sub-tasks, the ToC extraction and recognition is particularly useful for an easy navigation of book contents.

In this paper, we describe a general approach for script (and language) recognition from printed documents and for writer identification in handwritten documents. The method is based on a bag of visual word strategy where the visual words correspond to characters and the clustering is obtained by means of Self Organizing Maps (SOM). Unknown pages (words in the case of script recognition) are classified comparing their vectorial representations with those of one training set using a cosine similarity. The comparison is improved using a similarity score that is obtained taking into account the SOM organization of cluster centroids. Promising results are presented for both printed documents and handwritten musical scores.

This paper addresses the indexing and retrieval of mathematical symbols from digitized documents. The proposed approach exploits Shape Contexts (SC) to describe the shape of mathematical symbols. Starting from the vector space method, that is based on SC clustering, we explore the use of topological ordered clusters to improve the retrieval performance. The clustering is computed by means of Self-Organizing Maps that organize the clusters in two dimensional topologically ordered feature maps. The retrieval performance are compared with those obtained using the K-means clustering on a large collection of mathematical symbols gathered from the widely used INFTY database.

In this paper we compare three clustering methods used to perform word image indexing. The three methods are: the Self-Organizing Map (SOM), the Growing Hierarchical Self-Organizing Map (GHSOM), and the Spectral Clustering. We test these methods on a real data set composed of word images extracted from an encyclopedia of the XIX-th Century. The word images are grouped on the basis of the clustering methods and subsequently retrieved identifying the closest clusters to a query word. The accuracy of the methods is compared evaluating the performance of the word retrieval algorithm. From the experimental results we conclude that methods designed to automatically determine the number and the structure of clusters, such as GHSOM, are particularly suitable in the context represented by our data set.

We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of Self Organizing Maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.

We analyze a system for the retrieval of document images on the basis of layout similarity. Layout objects are extracted and represented with the XY tree. Page similarity is computed with a tree-edit distance algorithm. The peculiarity of the approach is the use of tree grammars to model the variations in the tree, which are due to segmentation algorithms or to structural differences between documents with similar layout. A few class-independent grammatical rules are used to modify each tree and obtain a reduced tree that is supposed to preserve the most relevant features of the page.

Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.

Incremental parsing gains its importance in natural language processing and psycholinguistics because of its cognitive plausibility. Modeling the associated cognitive data structures, and their dynamics, can lead to a better understanding of the human parser. In earlier work, we have introduced a recursive neural network (RNN) capable of performing syntactic ambiguity resolution in incremental parsing. In this paper, we report a systematic analysis of the behavior of the network that allows us to gain important insights about the kind of information that is exploited to resolve different forms of ambiguity. In attachment ambiguities, in which a new phrase can be attached at more than one point in the syntactic left context, we found that learning from examples allows us to predict the location of the attachment point with high accuracy, while the discrimination amongst alternative syntactic structures with the same attachment point is slightly better than making a decision purely based on frequencies. We also introduce several new ideas to enhance the architectural design, obtaining significant improvements of prediction accuracy, up to 25% error reduction on the same dataset used in previous work. Finally, we report large scale experiments on the entire Wall Street Journal section of the Penn Treebank. The best prediction accuracy of the model on this large dataset is 87.6%, a relative error reduction larger than 50% compared to previous results.

In this paper we describe a method for the expansionof training sets made by XY trees representing page layout.This approach is appropriate when dealing with page classificationbased on MXY tree page representations. The basicidea is the use of tree grammars to model the variationsin the tree which are caused by segmentation algorithms.A set of general grammatical rules are defined and used toexpand the training set. Pages are classified with a k - nnapproach where the distance between pages is computed bymeans of tree-edit distance.

This paper describes a system for efficient indexing and retrieval of words in collections of document images. The proposed method is based on two main principles: unsupervised prototype clustering, and string encoding for efficient string matching. During indexing, a self organizing map (SOM) is trained so as to cluster together similar symbols (character-like objects) in a sub-set of the documents to be stored. By using the trained SOM the words in the whole collection can be stored and represented with a fixed-length description that can be easily compared in order to score most similar words in response to a user query. The system can be automatically adapted to different languages and font styles. The most appropriate applications are for the processing of old documents (18th and 19th Centuries) where current OCRs have more difficulties. Experimental results describe three application scenarios having various levels of difficulty for current OCR systems.

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.

We develop novel algorithmic ideas for building a natural language parser grounded upon the hypothesis of incrementality, which is widely supported by experimental data as a model of human parsing. Our proposal relies on a machine learning technique for predicting the correctness of partial syntactic structures that are built during the parsing process. A recursive neural network architecture is employed for computing predictions after a training phase on examples drawn from a corpus of parsed sentences, the Penn Treebank. Our results indicate the viability of the approach and lay out the premises for a novel generation of algorithms for natural language processing which more closely model human parsing. These algorithms may prove very useful in the development of efficient parsers and have an immediate application in the construction of semiautomatic annotation tools.

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.

MOTIVATION: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network architectures with a fixed, and relatively short, input window of amino acids, centered at the prediction site. Although a fixed small window avoids overfitting problems, it does not permit capturing variable long-rang information. RESULTS: We introduce a family of novel architectures which can learn to make predictions based on variable ranges of dependencies. These architectures extend recurrent neural networks, introducing non-causal bidirectional dynamics to capture both upstream and downstream information. The prediction algorithm is completed by the use of mixtures of estimators that leverage evolutionary information, expressed in terms of multiple alignments, both at the input and output levels. While our system currently achieves an overall performance close to 76% correct prediction--at least comparable to the best existing systems--the main emphasis here is on the development of new algorithmic ideas. AVAILABILITY: The executable program for predicting protein secondary structure is available from the authors free of charge. CONTACT: [email protected], [email protected], [email protected], [email protected].

We introduce a probabilistic graphical model for supervised learning on databases with categorical attributes. The proposed belief network contains hidden variables that play a role similar to nodes in decision trees and each of their states either corresponds to a class label or to a single attribute test. As a major difference with respect to decision trees, the selection of the attribute to be tested is probabilistic. Thus, the model can be used to assess the probability that a tuple belongs to some class, given the predictive attributes. Unfolding the network along the hidden states dimension yields a trellis structure having a signal flow similar to second order connectionist networks. The network encodes context specific probabilistic independencies to reduce parametric complexity. We present a custom tailored inference algorithm and derive a learning procedure based on the expectation-maximization algorithm. We propose decision trellises as an alternative to decision trees in the context of tuple categorization in databases, which is an important step for building data mining systems. Preliminary experiments on standard machine learning databases are reported, comparing the classification accuracy of decision trellises and decision trees induced by C4.5. In particular, we show that the proposed model can offer significant advantages for sparse databases in which many predictive attributes are missing.

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.

Recurrent neural networks processing symbolic strings can be regarded as adaptive neural parsers. Given a set of positive and negative examples, picked up from a given language, adaptive neural parsers can effectively be trained to infer the language grammar. In this paper we use adaptive neural parsers to face the problem of inferring grammars from examples that are corrupted by a kind of noise that simply changes their membership.We propose a training algorithm, referred to as hybrid finite state filter (HFF), which is based on a parsimony principle that penalizes the development of complex rules.We report very promising experimental results showing that the proposed inductive inference scheme is indeed capable of capturing rules, while removing noise.

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.

Proposes a novel unified approach for integrating explicit knowledge and learning by example in recurrent networks. The explicit knowledge is represented by automaton rules, which are directly injected into the connections of a network. This can be accomplished by using a technique based on linear programming, instead of learning from random initial weights. Learning is conceived as a refinement process and is mainly responsible for uncertain information management. We present preliminary results for problems of automatic speech recognition.>

In this paper, we investigate the capabilities of local feedback multilayered networks, a particular class of recurrent networks, in which feedback connections are only allowed from neurons to themselves. In this class, learning can be accomplished by an algorithm that is local in both space and time. We describe the limits and properties of these networks and give some insights on their use for solving practical problems.

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.