Developers of large-scale document processing and image recognition systems are in need of a dynamically robust character segmentation component. Without this essential module, potential turn-key products will remain in the laboratory indefinitely. An experiment of evolving a biologically based neural image processing system which has the ability to isolate characters within an unstructured text image is presented. In this study, organisms are simulated using a genetic algorithm with the goal of learning the intelligent behavior required for locating and consuming text image characters. Each artificial life-form is defined by a genotype containing a list of interdependent control parameters which contribute to specific functions of the organism. Control functions include vision, consumption, and movement. Using asexual reproduction in conjunction with random mutation, a domain independent solution for text segmentation is sought. For this experiment, an organism's vision system utilizes a rectangular receptor field with signals accumulated using Gabor functions. The optimal subset of Gabor kernel functions for conducting character segmentation are determined through the process of evolution. From the results, two analyses are presented. A study of performance over evolved generations shows that qualifiers for the natural selection of dominant organisms increased 62%. The second analysis visually compares and discusses the variations of dominant genotypes form the first generation to the uniform genotypes resulting from the final generation.