Generic Feature Selection and Document Processing

This paper presents a generic features selection method and its applications on some document analysis problems.The method is based on a genetic algorithm (GA), whose fitness function is defined by combining Adaboot classifiers associated with each feature. Our method is not linked to a classifier achieving the final recognition task; we have used a combination of weak classifiers to evaluate a subset of features. So we select features that can further be used in the most appropriate classifiers.This method has been tested on three applications: dropcaps classification, handwritten digits recognition and text detection. The results show the efficiency and robustness of the proposed approach.