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Handwriting 99 Multiplication on App Store

Handwriting 99 Multiplication on App Store

Copyright: © 2020 |Pages: 18
ISBN13: 9781799815549|ISBN10: 1799815544|EISBN13: 9781799815563
DOI: 10.4018/978-1-7998-1554-9.ch004
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MLA

Jiann-Ming Wu and Chao-Yuan Tien. "Handwriting 99 Multiplication on App Store." MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities, IGI Global, 2020, pp.110-127. https://doi.org/10.4018/978-1-7998-1554-9.ch004

APA

J. Wu & C. Tien (2020). Handwriting 99 Multiplication on App Store. IGI Global. https://doi.org/10.4018/978-1-7998-1554-9.ch004

Chicago

Jiann-Ming Wu and Chao-Yuan Tien. "Handwriting 99 Multiplication on App Store." In MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1554-9.ch004

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Abstract

The Modified NIST (MNIST) database, consisting of 70,000 handwritten digit images, in partition to 60,000 training patterns and 10,000 testing patterns, serves as a typical benchmark of evaluating performance of handwritten digit classification. After the LeNet CNNs model proposed by LeCun, researchers regarded this example as “Hello, World” in the field of deep learning. This chapter compares traditional approaches with the CNN model. The dataset of training and testing CNN models here is expanded to the Extension-MNIST (EMNIST) database. It will be employed to pre-train a CNN model for recognizing the handwritten digit image and installation on the iOS device. The user of the presented App can directly write digits on the touchscreen, and the smartphone instantly recognizes what were written. The pre-trained model subject to EMNIST database with a test accuracy of 99.4% has been integrated to an iOS App, termed as handwriting 99 multiplication, which has been successfully published on Apple's App Store.

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