A two-stage approach for table extraction in invoices

The automated analysis of administrative documents is an important field in document recognition that is studied for decades. Invoices are key documents among these huge amounts of documents available in companies and public services. Invoices contain most of the time data that are presented in tables that should be clearly identified to extract suitable information. In this paper, we propose an approach that combines an image processing based estimation of the shape of the tables with a graph-based representation of the document, which is used to identify complex tables precisely. We propose an experimental evaluation using a real case application

A Parallel, Backjumping Subgraph Isomorphism Algorithm Using Supplemental Graphs

This registry entry contains a reference to the code, data and experimental scripts needed to reproduce the subgraph isomorphism paper: Ciaran McCreesh and Patrick Prosser, "A Parallel, Backjumping Subgraph Isomorphism Algorithm using Supplemental Graphs". To appear at the 21st International Conference on Principles and Practice of Constraint Programming (CP 2015)

Towards 40 years of constraint reasoning

Research on constraints started in the early 1970s. We are approaching 40 years since the beginning of this successful field, and it is an opportunity to revise what has been reached. This paper is a personal view of the accomplishments in this field. We summarize the main achievements along three dimensions: constraint solving, modelling and programming. We devote special attention to constraint solving, covering popular topics such as search, inference (especially arc consistency), combination of search and inference, symmetry exploitation, global constraints and extensions to the classical model. For space reasons, several topics have been deliberately omitted.Partially supported by the Spanish project TIN2009-13591-C02-02 and Generalitat de Catalunya grant 2009-SGR-1434.Peer Reviewe

Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan

Informed selection and use of training examples for knowledge refinement.

Knowledge refinement tools seek to correct faulty rule-based systems by identifying and repairing faults indicated by training examples that provide evidence of faults. This thesis proposes mechanisms that improve the effectiveness and efficiency of refinement tools by the best use and selection of training examples. The refinement task is sufficiently complex that the space of possible refinements demands a heuristic search. Refinement tools typically use hill-climbing search to identify suitable repairs but run the risk of getting caught in local optima. A novel contribution of this thesis is solving the local optima problem by converting the hill-climbing search into a best-first search that can backtrack to previous refinement states. The thesis explores how different backtracking heuristics and training example ordering heuristics affect refinement effectiveness and efficiency. Refinement tools rely on a representative set of training examples to identify faults and influence repair choices. In real environments it is often difficult to obtain a large set of training examples, since each problem-solving task must be labelled with the expert's solution. Another novel aspect introduced in this thesis is informed selection of examples for knowledge refinement, where suitable examples are selected from a set of unlabelled examples, so that only the subset requires to be labelled. Conversely, if a large set of labelled examples is available, it still makes sense to have mechanisms that can select a representative set of examples beneficial for the refinement task, thereby avoiding unnecessary example processing costs. Finally, an experimental evaluation of example utilisation and selection strategies on two artificial domains and one real application are presented. Informed backtracking is able to effectively deal with local optima by moving search to more promising areas, while informed ordering of training examples reduces search effort by ensuring that more pressing faults are dealt with early on in the search. Additionally, example selection methods achieve similar refinement accuracy with significantly fewer examples

Analyse, représentation et optimisation de la circulation des avions sur une plate-forme aéroportuaire

Au cours des dernières décennies, la demande de trafic au niveau des aéroports a augmenté régulièrement à tel point que le trafic au sol est devenu critique pour la sécurité et l'efficacité des opérations aéroportuaires. Cette thèse propose une approche à deux niveaux pour l'analyse et l'optimisation du trafic avion au sol sur les aéroports. Elle est divisée en trois parties : - La première partie introduit la problématique générale et son environnement - La deuxième partie traite la gestion à moyen terme du trafic au sol des avions. Une approche globale pour estimer la capacité théorique et la capacité pratique du trafic avion est proposée. Celle-ci met en oeuvre une approche d'optimisation du flux dans un réseau qui conduit à la formulation de différents problèmes de programmation mathématique - La troisième partie traite du niveau tactique et une approche adaptative est développée pour définir les routes et les horaires associés aux mouvement d'arrivée ou de départ des avions. Une approche de résolution opérationnelle est alors proposée. ABSTRACT : The airport traffic demand has been increasing steadily over the las1 decades making ground traffic at airports a critical issue with respect to security and efficiency. This thesis presents a multilevel approach for the analysis and the optimisation of airport ground traffic operations. This thesis is divided in threes parts : - the first part introduces the overall problematic and its environment, - the second part of the thesis deals with the medium term planning level. A global approach to estimate the theoretical as well as the practical airside capacity at airports, is proposed. This approach is based on a mathematical network representation which allows to take into account the structure of the ground traffic system and the resolution of a set of large mathematical programming which can interact with traffic simulators. - the third part of the thesis deals with the short term tactical level. An adaptive approach to manage at the tactical level the routing and scheduling of arriving and departing traffic. An operational approach is then proposed to solve the corresponding optimization problem

Integrating artificial neural networks and constraint logic programming.

by Vincent Wai-leuk Tam.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 74-80).Chapter 1 --- Introduction and Summary --- p.1Chapter 1.1 --- The Task --- p.1Chapter 1.2 --- The Thesis --- p.2Chapter 1.2.1 --- Thesis --- p.2Chapter 1.2.2 --- Antithesis --- p.3Chapter 1.2.3 --- Synthesis --- p.5Chapter 1.3 --- Results --- p.6Chapter 1.4 --- Contributions --- p.6Chapter 1.5 --- Chapter Summaries --- p.7Chapter 1.5.1 --- Chapter 2: An ANN-Based Constraint-Solver --- p.8Chapter 1.5.2 --- Chapter 3: A Theoretical Framework of PROCLANN --- p.8Chapter 1.5.3 --- Chapter 4: The Prototype Implementation --- p.8Chapter 1.5.4 --- Chapter 5: Benchmarking --- p.9Chapter 1.5.5 --- Chapter 6: Conclusion --- p.9Chapter 2 --- An ANN-Based Constraint-Solver --- p.10Chapter 2.1 --- Notations --- p.11Chapter 2.2 --- Criteria for ANN-based Constraint-solver --- p.11Chapter 2.3 --- A Generic Neural Network: GENET --- p.13Chapter 2.3.1 --- Network Structure --- p.13Chapter 2.3.2 --- Network Convergence --- p.17Chapter 2.3.3 --- Energy Perspective --- p.22Chapter 2.4 --- Properties of GENET --- p.23Chapter 2.5 --- Incremental GENET --- p.27Chapter 3 --- A Theoretical Framework of PROCLANN --- p.29Chapter 3.1 --- Syntax and Declarative Semantics --- p.30Chapter 3.2 --- Unification in PROCLANN --- p.33Chapter 3.3 --- PROCLANN Computation Model --- p.38Chapter 3.4 --- Soundness and Weak Completeness of the PROCLANN Compu- tation Model --- p.40Chapter 3.5 --- Probabilistic Non-determinism --- p.46Chapter 4 --- The Prototype Implementation --- p.48Chapter 4.1 --- Prototype Design --- p.48Chapter 4.2 --- Implementation Issues --- p.52Chapter 5 --- Benchmarking --- p.58Chapter 5.1 --- N-Queens --- p.59Chapter 5.1.1 --- Benchmarking --- p.59Chapter 5.1.2 --- Analysis --- p.59Chapter 5.2 --- Graph-coloring --- p.63Chapter 5.2.1 --- Benchmarking --- p.63Chapter 5.2.2 --- Analysis --- p.64Chapter 5.3 --- Exceptionally Hard Problem --- p.66Chapter 5.3.1 --- Benchmarking --- p.67Chapter 5.3.2 --- Analysis --- p.67Chapter 6 --- Conclusion --- p.68Chapter 6.1 --- Contributions --- p.68Chapter 6.2 --- Limitations --- p.70Chapter 6.3 --- Future Work --- p.71Chapter 6.3.1 --- Parallel Implementation --- p.71Chapter 6.3.2 --- General Constraint Handling --- p.72Chapter 6.3.3 --- Other ANN Models --- p.73Chapter 6.3.4 --- Other Domains --- p.73Bibliography --- p.74Appendix A The Hard Graph-coloring Problems --- p.81Appendix B An Exceptionally Hard Problem (EHP) --- p.18