15-06-2010, 04:42 PM
Abstract:
Handwriting recognition is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface.
The feed forward Back Propagation Neural Network (BPNN) with one input layer, two hidden layers and one output layer is selected for training.
Abstractâ€A back-propagation neural network with one hidden layer was used to create an adaptive character recognition system. The system was trained and evaluated with printed text, as well as several different forms of handwriting provided by both male and female participants.
Experiments tested:
The effect of set size on recognition accuracy with printed text.
The effect of handwriting style on recognition accuracy.
Results showed reduced accuracy in recognizing printed text when differentiating between more than 12 characters. The handwriting style of the subjects had varying and drastic effects on recognition accuracy which illuminated some of the problems with the systems character encoding.
