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Digit Recognition Using Machine Learning

Digit Recognition Using Machine Learning. Matheus Lelis University of Massachusetts: Dartmouth. Abstract.

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Digit Recognition Using Machine Learning

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  1. Digit Recognition Using Machine Learning Matheus LelisUniversity of Massachusetts: Dartmouth

  2. Abstract • The goal of this research project is to use an artificial neural network machine learning algorithms with back propagation to develop a program which will recognize handwritten letters and numbers.

  3. Background Artificial Neural Network • An artificial neural network learning algorithm is a learning algorithm that is inspired by the structure and functional aspects of biological neural networks. • Computations are structured in terms of an interconnected group of artificial neurons, processing information using a connectionist approach to computation.

  4. Problem Character Recognition • Reading in the images of handwritten numbers and letters and outputting the machine-encoded version. • Adding distortion to try to solve CAPTCHAs.

  5. Progress The machine was written using MATLAB works in two steps. 1st step – runs through a set of data and learns and sets weight 2nd step – runs through new data and tries to guess. It takes in a matrix of data with images 20px by 20px

  6. Issues • Finding/Creating new data to test the machine with. • Teaching the machine to work with letters, only works with numbers for now. • Adding the distortion and test out the CAPTCHAs.

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