Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

I want out of the labels : how Chuck Palahniuk's characters challenge the dominant discourse

The purpose of this study was to explore postmodern identity in the work of Chuck Palahniuk. The characters within Palahniuk's text Invisible Monsters realize the manner in which identity is a construct, and their response is to oppose and redefine it. In my research, I looked at how postmodern identity is defined by some of the leading critical thinkers in the field, and then I applied their thought to Palahniuk's characters. I showed how these characters come to understand the process by which society defines them, and with that realization, they oppose its totalizing definitions. The characters deconstruct the natural attitudes society has towards identity, and they reveal that it is in some way possible to create a unique identity that is not easily definable by the ruling discourse. I concluded that his attention to identity highlights Palahniuk's concern for the place, identity, and influence of his generation.