Recognition of Handwritten Numbers Using Artificial Neural Networks Alexander Gutierrez Mentor: Amar Raheja California State Polytechnic University, Pomona Computers are widely used for different applications ranging from communication to the modeling of supernovae. However, despite the capability and speed of modern computers and even supercomputers, the human brain has yet to be surpassed in efficiency in tasks such as speech and visual recognition. While many tasks are automated in the modern world, trivial tasks for humans such as reading a license plate cannot be done reliably with even the most advanced computers. Artificial neural networks are an important and influential approach to attempting to bridge this gap in technology. In this experiment, several configurations of multilayer perceptrons, a type of artificial neural network, are tested using images of seed germination trials with numbers written for batch identification. The purpose of this study is to analyze efficiency ratings of different architectures of multilayer perceptrons in recognizing handwritten numbers and develop an application that can be used to read numbers associated with seed batch specimens. A number of configurations were trained and tested using the MNIST database of digits where number of layers, nodes in each layer, and training set size were varied. From the survey, a two-layer network with 300 hidden nodes was chosen for further training and testing. This network was trained with the conjugate gradient algorithm and achieved a digit accuracy of over 95% with a larger training set. Image preprocessing was added in order to create an application to fully automate the identification of the seed germination photographs.
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