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Dynamic Hand Written Character Recognition

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This project aims to develop a novel methodology for recognizing handwritten characters without relying on traditional training processes used in classical neural networks. By leveraging the dynamics of biological neural systems, we simulate an elastic body in a force field to capture the 2-D information of written digits. Our approach is validated using the MNIST database, and shows promise for broader applications beyond digit recognition, including 3-D adaptations. We target smaller, repeating features to enhance efficiency and reduce computational costs.

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Dynamic Hand Written Character Recognition

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  1. Dynamic Hand Written Character Recognition Shi-Ting Zhou

  2. Project Goal • Devise a new approach in recognizing hand written characters without training process in respective classical neural network approaches. • Validate the proposed technique by testing on hand written digits

  3. Motivation • Classical ANN does not possess the merits of biological neural systems • Real biological neural systems are dynamical • Classical ANN approaches ignore 2-D information of inputs

  4. Methodology • Devise a simulator capable of simulating the dynamics of elastic body in attracting force field • Build templates of written digits • Test on the MNIST DATABASEof handwritten digits (“http://yann.lecun.com/exdb/mnist/”)

  5. Neural Network? • Can be implemented in the form of recurrent neural network.

  6. Discussion • Potential usage on other type of recognition task • Can have a 3-D version. 1-D lines become 2-D manifolds • Problem: Computationally intensive if have a lot of templates • Solution: Targeting at smaller and repeating features instead of entire symbol

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