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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 cases where little data is available. In this study, the authors propose a novel method for learning siamese neural network which employs a special structure to predict the similarity between handwritten Chinese characters and template images. The optimisation of siamese neural network can be treated as a simple binary classification problem. When the training process finished, the powerful discriminative features will help to generalise the predictive power not just to new data, but to entirely new classes that never appear in the training set. Experiments performed on the ICDAR-2013 offline HCCR datasets have shown that the proposed method has a very promising generalisation ability for new classes that never appear in the training set.
ISSN:2051-3305
2051-3305
DOI:10.1049/joe.2019.0895