Gabor Features Assist Semantic Feature Learning for Handwritten Formula Symbol Recognition

At present, the recognition of formula symbols is very challenging. Since a large number of similarities and a variety of styles in the standard library, the overall recognition rate of formula symbols is difficult to further improve. In order to deal with these problems, this paper proposes an off-...

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Bibliographic Details
Published in2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC) pp. 1 - 4
Main Authors Fang, Dingbang, Feng, Gui, Yang, Hengjie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2019
Subjects
Classification algorithms
Convolution
Feature extraction
feature fusion
Handwriting recognition
image process
Kernel
Libraries
neural network
Training
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Summary:At present, the recognition of formula symbols is very challenging. Since a large number of similarities and a variety of styles in the standard library, the overall recognition rate of formula symbols is difficult to further improve. In order to deal with these problems, this paper proposes an off-line multi-directional feature fusion decision discriminant algorithm, called MFFD. The novelty lies in the construction of the MMFD based on the convolutional neural network. In addition, we explore the directional gradient feature that facilitates the classification of handwritten formula symbols. The standard mathematical formula symbol library provided by the CROHME would verify the proposed algorithm. The error rates of CROHME2014 and CROHME2016 are 8.28% and 6.88%, respectively, which is higher than that of existing algorithms.
ISBN:1728111897
9781728111896
ISSN:2377-844X
DOI:10.1109/ICEIEC.2019.8784656