A framework for parameterization in computer vision algorithms is evaluated by optimizing ten parameters of the text detection for semantic indexing algorithm preposed by Wolf et al. The Fuzzy ARTMAP neural network is used for generalization, offering much faster learning than in a previous tabular implementation. Difficulties in using a continuous action space are overcome by employing the DIRECT method for global optimization without derivatives. The chosen parameters are evaluated using metrics of recall and precision, and are shown to be superior to the parameters previously recommended.