OBJECT ORIENTED ARTIFICIAL NEURAL NETWORK SIMULATOR IN TEXT AND SYMBOL RECOGNITION

dc.contributor.authorPiszcz, Alan
dc.contributor.authorIshaq, Naseem; Advisor
dc.contributor.authorNovillo, Jorge E; Reviewer
dc.contributor.authorSengupta, Saumendra; Reviewer
dc.date.accessioned2018-08-07T17:39:57Z
dc.date.available2018-08-07T17:39:57Z
dc.date.issued1993
dc.descriptionMaster 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.en_US
dc.description.abstractObjected 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 symbolsen_US
dc.identifier.urihttp://hdl.handle.net/1951/70362
dc.language.isoenen_US
dc.subjectobject oriented languagesen_US
dc.subjectneural networksen_US
dc.subjectOptical Character Recognition (OCR)en_US
dc.titleOBJECT ORIENTED ARTIFICIAL NEURAL NETWORK SIMULATOR IN TEXT AND SYMBOL RECOGNITIONen_US
dc.typeThesisen_US
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