Objected oriented languages and artificial neural networks are new areas of research and development. This thesis investigates the application of artificial neural networks using an object oriented C++ backpropagation simulator. The application domain investigated is hand printed text and engineering symbol recognition. An object oriented approach to the simulator allows other simulator paradigms to reuse a large body of the object classes developed for this particular application. The review and implementation of image feature extraction methodologies is another area researched in this
paper. Four feature techniques are researched, developed, applied and tested, using digits, upper case alphabet characters and engineering symbol images. Final implementation and testing of the feature extraction methods with a baseline technique is analyzed for applicability in the domain of hand printed text and engineering symbols
Master of Science Thesis in Computer Science, Department of Computer Science, SUNY College of Technology at Utica/Rome. Approved and recommended for acceptance as a project in partial fulfillment of the requirements for the degree of Master of Science in Computer Science. Submitted by author to digital archive, August 2018.