Nicolas Sidère

Vector Representation of Graphs: Application to the Classification of Symbols and Letters

In this article we present a new approach for the classification of structured data using graphs. We suggest to solve the problem of complexity in measuring the distance between graphs by using a new graph signature. We present an extension of the vector representation based on pattern frequency, which integrates labeling information. In this paper, we compare the results achieved on public graph databases for the classification of symbols and letters using this graph signature with those obtained using the graph edit distance.

A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition

In recent years graph embedding has emerged as a promising solution for enabling the expressive, convenient, powerful but computational expensive graph based representations to benefit from mature, less expensive and efficient state of the art machine learning models of statistical pattern recognition. In this paper we present a comparison of two implicit and three explicit state of the art graph embedding methodologies. Our preliminary experimentation on different chemoinformatics datasets illustrates that the two implicit and three explicit graph embedding approaches obtain competitive performance for the problem of graph classification.

A Vectorial Representation for the Indexation of Structural Informations

This article presents a vectorial representation of structured data to reduce the complexity of dissimilarity computations in an information retrieval context. This representation enables, via a computation of an adapted measure, to approximate the distance between structural representations in both context of distance between graphs and searching occurrences of subgraphs. Preliminary results show that the proposed representation offers comparable performance with those of the literature.

Interactive layout analysis, content extraction, and transcription of historical printed books using Pattern Redundancy Analysis

This article describes the work performed in the Pattern Redundancy Analysis for Document Image Indexing and Transcription research project. The project focused on layout analysis, text/graphics separation, optical character recognition (OCR), and text transcription processes dedicated to old and precious books. The originality of this work relies on the analysis and exploitation of pattern redundancy in documents to enable the efficient indexing and quick transcription of books and the identification of typographic materials. For these purposes, we have developed two software packages. The first, AGORA, performs page layout analysis, text/graphics separation, and pattern (letterform) extraction simultaneously. These patterns are then processed to group similar patterns together in single clusters so that different letterforms of a book can be extracted and analysed to compute redundancy rates. This process allows a significant reduction of the number of letterforms to be recognized. Once the clustering of letterforms is done, a user may assign a label to each cluster using the second software, RETRO. Labels are then automatically assigned to each corresponding character to perform the text transcription of the whole book. Thus, if 90% of the letterforms are detected as redundant, only one character out of ten must be labelled by the user to transcribe the book. Moreover, this transcription method allows us to deal easily with the special characters that appear frequently in old books. It is also possible to use our clustering approach to extract and create new font packages from specific printing material (e.g. from rare books printed with particular types or woodblocks). These new font packages could be incorporated into the training step of optical fonts recognition methods to improve the recognition results of OCRs on rare or specific books. The identification of typographic materials could also be useful for the study of both the aesthetic (such as how the thickness and shape of printing types evolved from the 15th to the mid-16th century) and economic aspects of printing historically. Until the second half of the 16th century, for instance, printing types circulated among workshops, and printers frequently sold or lent types to their fellows.

Document Classification in a Non-stationary Environment: A One-Class SVM Approach

In this paper, we investigate a specific area of document classification in which the documents come as a flow over the time. Moreover, the exact number of classes of document to deal with is not known from the beginning and could evolve over the time. To be able to perform classification task in such area, we need specific classifiers that are able to perform incremental learning and change their modeling over the time. More specifically, we are focusing our study on SVM approaches, known to perform well, and for which incremental (i-SVM) procedures exist. Nevertheless, most of them are only able to deal with a fixed number of classes. So we designed a new incremental learning procedure based on one-class SVMs. This one is able to improve its classification accuracy over the time, with the arrival of new labeled data, without performing any complete retraining. Moreover, when instances are coming with a previously unknown label (appearance of a new class), the training procedure is able to modify the classifier model to recognize this corresponding new kind of documents. To investigate this area, waiting for collecting documents images as a flow, we did first experiments on the Optical Recognition of Handwritten Digits Data Set. These experiments show that our incremental approach is able: to perform, at each time, as well as a static one-class classifier fully retrained using all previously seen data, to model very quickly and efficiently new incoming classes.

A semi-automatic groundtruthing framework for performance evaluation of symbol recognition and spotting systems

In this paper, we are interested with the groundtruthing problem for performance evaluation of symbol recognition & spotting systems. We propose a complete framework based on user interaction scheme through a tactile device, exploiting image processing components to achieve groundtruthing of real-life documents in an semi-automatic way. It is based on a top-down matching algorithm, to make the recognition process less sensitive to context information. We have developed a specific architecture to achieve the recognition in constraint time, working with a sub-linear complexity and with extra memory cost.

A Compliant Document Image Classification System Based on One-Class Classifier

Document image classification in a professional context requires to respect some constraints such as dealing with a large variability of documents and/or number of classes. Whereas most methods deal with all classes at the same time, we answer this problem by presenting a new compliant system based on the specialization of the features and the parametrization of the classifier separately, class per class. We first compute a generalized vector of features based on global image characterization and structural primitives. Then, for each class, the feature vector is specialized by ranking the features according a stability score. Finally, a one-class K-nn classifier is trained using these specific features. Conducted experiments reveal good classification rates, proving the ability of our system to deal with a large range of documents classes.