一种用于手写数字识别的用户自适应深度机器学习方法

2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII) Pub Date : 2018-07-01 DOI:10.1109/ICKII.2018.8569150

Huijie Zhang, Qiyu Wang, Xin Luo, Yufang Yin, Yingsong Chen, Zhouping Cui, Quan Zhou

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引用次数: 1

摘要

随着机器学习的发展，手写识别问题越来越受到人们的关注。本文提出了一种用户自适应的HWR方法，用于仅需要识别手写数字和少数有限字符的应用。五种类型的CNN(卷积神经网络)分类器分为三个步骤:数字识别、字符串类型分类器和字符串识别。实验结果表明，该方法能够对数字和少数受限字符进行HWR。

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A user-adaptive deep machine learning method for handwritten digit recognition

The HWR (handwritten recognition) problem gains more attention with the development of machine learning. In this work, a user-adaptive HWR method is purposed for the application when only handwritten digits and few limited characters need to be recognized. Five types of CNN (Convolutional Neural Network) classifier are used in three steps: digits recognition, string-type classifier and string recognition. Experiment results show that the purposed method is capable of HWR for digits and few limited characters.

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2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)

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