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Handwriting Recognition System: Comparative Analysis of Artificial Neural Network and Convolutional Neural Network Models

head shot of emily pfau

Name: Emily Pfau
Major: Computer Science
Minor: Political Science
Advisor: Kowshik Bhowmik

Handwriting recognition has been a challenging research area with many functions in everyday life. An application is capable of recognizing letters and digits can convert handwritten text into digital format, allowing those who prefer not too type or unable to type to take notes by hand. This paper takes two different neural networks, artificial neural network and Convolutional neural network, and shows the steps both networks take to recognizes English handwritten letters and transfer it to regular typeface. This is done by the use of the library Pytorch and EMNIST Dataset. This paper looks at the history, application, architecture and training process of these two different handwriting recognition programs.

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Posted in Comments Enabled, Independent Study, Symposium 2022.


3 responses to “Handwriting Recognition System: Comparative Analysis of Artificial Neural Network and Convolutional Neural Network Models”

  1. Anabelle Andersen says:

    Woohoo! It’s incredible to see your hard work pay off! What a lovely four years it’s been, so glad to have you in my life!

  2. Chan Sok Park says:

    Emily, congratulations on your important achievement! Wishing you the best for what’s ahead in your life journey.

  3. Leah Wilcox says:

    I loved watching you work on this; it was like watching magic. I am always in awe of the things you can do. This was amazing!

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Related Areas of Study

Political Science

The study of power, with concentrations in U.S. politics, international relations, political theory and comparative politics.

Major Minor

Computer Science

Solve complex problems with creative solutions using computer programming and applications

Major Minor

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