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Recognition of Online Handwritten Math Symbols Using Deep Neural Networks
Hai DAI NGUYEN Anh DUC LE Masaki NAKAGAWA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2016/12/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Pattern Recognition
CNN, BLSTM, gradient features, dropout, maxout,
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This paper presents deep learning to recognize online handwritten mathematical symbols. Recently various deep learning architectures such as Convolution neural networks (CNNs), Deep neural networks (DNNs), Recurrent neural networks (RNNs) and Long short-term memory (LSTM) RNNs have been applied to fields such as computer vision, speech recognition and natural language processing where they have shown superior performance to state-of-the-art methods on various tasks. In this paper, max-out-based CNNs and Bidirectional LSTM (BLSTM) networks are applied to image patterns created from online patterns and to the original online patterns, respectively and then combined. They are compared with traditional recognition methods which are MRFs and MQDFs by recognition experiments on the CROHME database along with analysis and explanation.