Data categorization for a context return applied to logical document structure recognition

The purpose of this work is to develop a pattern recognition system simulating the human vision. A transparent neural network, with context returns is used. The context returns consist in using global vision to correct local vision (i.e. input data are corrected according to neural network outputs). In order not to compute all the input features during these context returns, a filter-based method was designed to organize the features in clusters. This allows finding a good subset of input features during each cycle, which reduce the computations. The method interest is shown in the case of logical document structure retrieval.