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dc.contributor.authorVillalobos, Andy
dc.date.accessioned2020-04-30T10:46:50Z
dc.date.available2020-04-30T10:46:50Z
dc.date.issued2019-04-25
dc.identifier.urihttps://scholars.carroll.edu/handle/20.500.12647/7295
dc.description.abstractHow does a computer recognize individual letters and numbers? One method to approach this problem is to use a Convolutional Neural Network (CNN). To explore this method, we are investigating the accuracy of CNNs in classifying handwritten text relative to a variety of parameters that influence predictive accuracy. In particular, our focus is a dataset of handwritten Arabic letters, which have more similarity to each other than do handwritten Arabic numerals. Some of the parameters we are experimenting with are the size of the “window” that a CNN "views" an image with, or how separated these "viewing windows” are. Other parameters include the number of “window types” that a CNN might use to classify an image in conjunction with these other structural hyperparameters. We are attempting to achieve a classification accuracy of greater than 95% for Arabic handwriting by altering structural hyperparameters, which tune the way that a CNN “views” images. Ultimately, we would like to build a tool that can very accurately tell the difference between handwritten digits and letters without human intervention.
dc.titleComputer Vision and Handwriting Analysis
carrollscholars.object.disciplinesArtificial Intelligence and Robotics; Numerical Analysis and Scientific Computing; Systems Architecture
carrollscholars.legacy.itemurlhttps://scholars.carroll.edu/surf/2019/all/12
carrollscholars.legacy.contextkey14314370
carrollscholars.object.majorMathematics
carrollscholars.object.fieldofstudyComputer Science
carrollscholars.location.campusbuildingCampus Center - Ross
carrollscholars.event.startdate4/25/2019 11:00
carrollscholars.event.enddate4/25/2019 11:15
carrollscholars.contributor.emailavillalobos@carroll.edu
carrollscholars.contributor.institutionCarroll College


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