Automated recognition of unconstrained handwriting continues to be a challenging research task. In addition to the errors caused by image quality, image features, segmentation, and recognition, in this paper we have also explored the influence of image complexity on handwriting recognition and compared humans' versus machines' recognition. We describe a new methodology that will exploit the gap between the abilities of humans and computers in reading handwritten text images and investigate the influence of handwritten image complexity and Gestalt Laws of perception on this gap. Experimental results are presented and compared for image density and perimetric complexity of handwritten challenges. We make use of current challenges in handwriting recognition for applications in Cyber security.