Learning templates from fuzzy examples in structural pattern recognition

Fuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recognition. FAG has the advantage that we can combine several possible definition into a single template. However, the template require a human expert to define. In this paper, we propose an algorithm that can; from a number of fuzzy instances, find a template that can be matched to the patterns by the original matching metric.published_or_final_versio

Semantic Label and Structure Model based Approach for Entity Recognition in Database Context

International audience—This paper proposes an entity recognition approach in scanned documents referring to their description in database records. First, using the database record values, the corresponding document fields are labeled. Second, entities are identified by their labels and ranked using a TF/IDF based score. For each entity, local labels are grouped into a graph. This graph is matched with a graph model (structure model) which represents geometric structures of local entity labels using a specific cost function. This model is trained on a set of well chosen entities semi-automatically annotated. At the end, a correction step allows us to complete the eventual entity mislabeling using geometrical relationships between labels. The evaluation on 200 business documents containing 500 entities reaches about 93% for recall and 97% for precision

Uma Abordagem para Comparação de Mapas Conceituais utilizando Correspondência de Grafos

O problema de correspondência de grafos (PCG) consiste em um problema formulado em Otimização Combinatória para a comparação estrutural de grafos, a partir da identificação desimilaridades. Proposto inicialmente para aplicações em reconhecimento de imagens, pretende-se neste trabalho, adaptá-lo para uma aplicação em recuperação inteligente de informação, a saber, a comparação de mapas conceituais em representação de conhecimento, assim como investigar autilização de algoritmos heurísticos e exatos para a sua resolução

Entity Local Structure Graph Matching for Mislabeling Correction

International audienceThis paper proposes an entity local structure comparison approach based on inexact subgraph matching. The comparison results are used for mislabeling correction in the local structure. The latter represents a set of entity attribute labels which are physically close in a document image. It is modeled by an attributed graph describing the content and presentation features of the labels by the nodes and the geometrical features by the arcs. A local structure graph is matched with a structure model which represents a set of local structure model graphs. The structure model is initially built using a set of well chosen local structures based on a graph clustering algorithm and is then incrementally updated. The subgraph matching adopts a specific cost function that integrates the feature dissimilarities. The matched model graph is used to extract the missed labels, prune the extraneous ones and correct the erroneous label fields in the local structure. The evaluation of the structure comparison approach on 525 local structures extracted from 200 business documents achieves about 90% for recall and 95% for precision. The mislabeling correction rates in these local structures vary between 73% and 100%

Inexact graph matching for entity recognition in OCRed documents

International audienceThis paper proposes an entity recognition system in image documents recognized by OCR. The system is based on a graph matching technique and is guided by a database describing the entities in its records. The input of the system is a document which is labeled by the entity attributes. A first grouping of those labels based on a function score leads to a selected set of candidate entities. The entity labels which are locally close are modeled by a structure graph. This graph is matched with model graphs learned for this purpose. The graph matching technique relies on a specific cost function that integrates the feature dissimilarities. The matching results are exploited to correct the mislabeling errors and then validate the entity recognition task. The system evaluation on three datasets which treat different kind of entities shows a variation between 88.3% and 95% for recall and 94.3% and 95.7% for precision

A Graph-Based Algorithm to Determine Protein Structure from Cryo-EM Data

Cryo-electron microscopy: cryo-EM) provides 3D density maps of proteins, but these maps do not have sufficiently high resolution to directly yield atomic-scale models. Previous work has shown that features known as secondary structures can be located in these density maps. A second source of information about proteins is sequence analysis, which predicts locations of secondary structures along the protein sequence but does not provide any information about the 3D shape of the protein. This thesis presents a graph-based algorithm to find the correspondence between the secondary structures in the density map and sequence. This provides an ordering of secondary structures in the 3D density map, which can be used in building an atomic-scale model of the protein

A Google-inspired error-correcting graph matching algorithm

Graphs and graph algorithms are applied in many different areas including civil engineering, telecommunications, bio-informatics and software engineering. While exact graph matching is grounded on a consolidated theory and has well known results, approximate graph matching is still an open research subject. This paper presents an error tolerant approximated graph matching algorithm based on tabu search using the Google-like PageRank algorithm. We report preliminary results obtained on 2 graph data benchmarks. The first one is the TC-15 database [14], a graph data base at the University of Naples, Italy. These graphs are limited to exact matching. The second one is a novel data set of large graphs generated by randomly mutating TC-15 graphs in order to evaluate the performance of our algorithm. Such a mutation approach allows us to gain insight not only about time but also about matching accuracy

On-line Chinese character recognition.

by Jian-Zhuang Liu.Thesis (Ph.D.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (p. 183-196).Microfiche. Ann Arbor, Mich.: UMI, 1998. 3 microfiches ; 11 x 15 cm

Contribution aux méthodes de reconnaissance structurelle de formes (approche à base de projections de graphes)

Les travaux exposés dans cette thèse portent sur une contribution aux techniques de projection de graphes, appliquées à la reconnaissance de formes, visant à tirer parti de la richesse des méthodes structurelles et de l efficacité des outils statistiques. Nous présentons une nouvelle projection s inscrivant dans la catégorie des sondages de graphes. La première contribution de cette thèse porte sur l encapsulation de la topologie du graphe dans une représentation vectorielle, en s appuyant sur le dénombrement de motifs (sous-graphes) issus d un lexique généré indépendamment du contexte. Ces motifs permettent de minimiser les pertes de l information topologique lors de la projection. La deuxième contribution porte sur l intégration de l information relative aux étiquettes au sein de notre projection par l adjonction de leurs dénombrements. Aux problèmes liés à la nature et la variabilité des attributs, nous proposons deux solutions dans le but de constituer des classes d étiquettes moins nombreuses. La première consiste à discrétiser les attributs numériques puis à les combiner. La deuxième vise à former ces classes par un partitionnement global de l ensemble des étiquettes. Ces propositions sont ensuite évaluées sur différentes bases de graphes et dans différents contextes.The work exposed in this thesis focuses on a contribution to techniques of graph embedding, applied to pattern recognition, aiming to take advantages of the richness of structural methods and the efficiency of statistical tools. We present a new embedding, joining the category of graph probing. The first contribution of this thesis deals with the embedding of the graph topology in a vectorial representation, based on the counting of patterns (subgraphs) stemming of a lexicon generated independently of the context. These patterns permit the minimization of losses of the topological information during the embedding. The second contribution focuses on the integration of the information related to labels inside our embedding by adding their counting. To deal with problems linked to the nature and the variability of the attributes, we suggest two solutions to reduce the number of label classes. The first one consists of discretizing numeral attributes and combining them The second one aims to build these classes by a global clustering on the set of labels. Then, these proposals are evaluated on different datasets of graphs and in different contexts.TOURS-Bibl.électronique (372610011) / SudocSudocFranceF