Deep template matching for offline handwritten Chinese character recognition

Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success. However, the process of learning discriminative features for image recognition is difficult in...

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Bibliographic Details
Published in:	Journal of engineering (Stevenage, England) Vol. 2020; no. 4; pp. 120 - 124
Main Authors:	Li, Zhiyuan, Xiao, Yi, Wu, Qi, Jin, Min, Lu, Huaxiang
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 01.04.2020 Wiley
Subjects:	computer vision computer vision tasks convolutional neural nets convolutional neural networks deep template feature extraction handwritten character recognition ICDAR‐2013 offline HCCR datasets image classification image recognition learning (artificial intelligence) offline handwritten Chinese character recognition powerful discriminative features predictive power remarkable achievements Research Article Siamese neural network simple binary classification problem template images training process training set ICDAR-2013 offline HCCR datasets training set handwritten character recognition image classification offline handwritten Chinese character recognition deep template template images computer vision tasks training process predictive power Siamese neural network feature extraction simple binary classification problem computer vision remarkable achievements convolutional neural nets convolutional neural networks learning (artificial intelligence) image recognition powerful discriminative features
ISSN:	2051-3305, 2051-3305
Online Access:	Get full text
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