Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Handwritten numeral recognition is a classical and important task in the computer vision area. We propose two novel deep learning models for this task, which combine the edge extraction method and Siamese/Triple network structures. We evaluate the models on seven handwritten numeral datasets and the...

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Bibliographic Details
Published inIEICE Transactions on Information and Systems Vol. E103.D; no. 3; pp. 720 - 723
Main Authors JIANG, Weiwei, ZHANG, Le
Format Journal Article
LanguageEnglish
Published Tokyo The Institute of Electronics, Information and Communication Engineers 01.03.2020
Japan Science and Technology Agency
Subjects
Computer vision
convolutional neural network
Deep learning
Handwriting recognition
handwritten numeral recognition
Machine learning
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Summary:Handwritten numeral recognition is a classical and important task in the computer vision area. We propose two novel deep learning models for this task, which combine the edge extraction method and Siamese/Triple network structures. We evaluate the models on seven handwritten numeral datasets and the results demonstrate both the simplicity and effectiveness of our models, comparing to baseline methods.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0916-8532
1745-1361
DOI:10.1587/transinf.2019EDL8199