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Pattern Recognition of Handwritten English Characters
Abstract
After success of a total solution to handwriting 99 multiplication by deep learning, this chapter further addresses on the problem with increased complexity. In addition to handwritten digital dataset, the EMNIST database provides multiple balanced or unbalanced datasets. These datasets contain different combinations of handwritten digit and letter images. It is believed that well trained deep CNNs can handle unbalanced datasets, so this chapter chose By_Class of EMNIST database as a dataset to increase the difficulty of problem solving and extend the application of iOS Apps. This chapter discusses classification of handwritten English character, including uppercase and lowercase, data audition due to requirement of further improvement, and online tests on iOS devices. After a long time of training, the developer got the pre-trained CNN model. For 58,405 testing images, the recognition accuracy rate was as high as 97.0%.
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