Luiz Eduardo Soares de Oliveira

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—A modular system to recognize handwritten numerical strings is proposed. It uses a segmentation-based recognition approach and a Recognition and Verification strategy. The approach combines the outputs from different levels such as segmentation, recognition, and postprocessing in a probabilistic model. A new verification scheme which contains two verifiers(More)
In this paper we describe a synthetic database composed of 273,452 handwritten touching digits pairs to assess segmentation algorithms. It contains several different kinds of touching and it was generated by connecting 2,000 images of isolated digits extracted from the NIST SD19. In order to get a better insight on the proposed database and establish some(More)
In this paper a methodology for feature selection for the handwritten digit string recognition is proposed. Its novelty lies in the use of a multiobjective genetic algorithm where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate fitness and the use of a validation database to identify the subsets(More)
In this paper, the impact on fuzzy ARTMAP performance of decisions taken for batch supervised learning is assessed through computer simulation. By learning different real-world and synthetic data, using different learning strategies, training set sizes, and hyper-parameter values, the generalization error and resources requirements of this neural network(More)
In this paper two approaches of genetic algorithm for feature subset selection are compared. The first approach considers a simple genetic algorithm (SGA) while the second one takes into account an iterative genetic algorithm (IGA) which is claimed to converge faster than SGA. Initially, we present an overview of the system to be optimized and the(More)
This paper describes an implicit segmentation-based method for recognition of strings of characters (words or numerals). In a two-stage HMM-based method, an implicit segmentation is applied to segment either words or numeral strings, and in the verification stage, foreground and background features are combined to compensate the loss in terms of recognition(More)
Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the clas-sifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The(More)
Although it shows enormous potential as a feature extractor, 2D principal component analysis (2DPCA) produces numerous coefficients. Using a feature-selection algorithm based on a multiobjective genetic algorithm to analyze and discard irrelevant coefficients offers a solution that considerably reduces the number of coefficients, while also improving(More)
Forest species can be taxonomically divided into groups, genera, and families. This is very important for an automatic forest species classification system, in order to avoid possible confusion between species belonging to two different groups, genera, or families. A common problem that researchers in this field very often face is the lack of a(More)