Authors:
Kuniko Yamada
1
and
Harumi Murakami
2
Affiliations:
1
Graduate School for Creative Cities, Osaka City University, Osaka, Japan
;
2
Graduate School of Informatics, Osaka Metropolitan University, Osaka, Japan
Keyword(s):
Math Expression Image, Concise Math Expression, Math Expression Filter, SVM, CNN.
Abstract:
Even though the web is an effective resource to search for math expressions, finding appropriate ones among the obtained documents is time-consuming. Therefore, we propose a math expression filter that presents appropriate images for such searches. We call an appropriate math expression a concise math expression, such as ˆfh(x) = 1Nh ∑ni=1K(x−xih), written in a compact form whose content can be interpreted by the math expression itself. We determined the conditions satisfied by a concise math expression and developed classifiers that discriminate the images of concise math expressions from web images using supervised machine learning methods based on these conditions. We performed two experiments: Experiment 1 used methods other,than deep learning, and Experiment 2 used deep learning. A convolutional neural network (CNN) with transfer learning and fine tuning by VGG16 shows high performance with an obtained F-measure of 0.819. We applied this filter to a task that presents math expre
ssion images by entering mathematical terms into a web search engine as queries. All of the evaluation metrics outperformed the previous study, including F-measure, MAP, and MRR.
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