2020 Fiscal Year Annual Research Report
A Sequence-to-sequence Model based Dissimilarity Measurement for Clustering Structural Data
| Project/Area Number |
18K18068
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
NGUYENTUAN CUONG 東京農工大学, 工学(系)研究科(研究院), 特任助教 (10814246)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Keywords | handwritten answers / clustering / mathematical expressions / handwriting recognition |
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| Outline of Annual Research Achievements |
We have finished applying the proposed generative sequence dissimilarity for clustering of handwritten mathematical answers. The proposed method outperforms other clustering methods which do not focus on local features such as Deep Embedded Clustering and Siamese Networks. The proposed method also superior to the hierarchical feature representations by Convolutional Neural Networks with Weakly Supervised learning. We have applied the method for clustering online handwritten mathematical expressions and show that the proposed metric is better than the edit distance metric. We continue to apply the clustering method for a large-scale database of offline handwritten mathematical answers collected from the preliminary examination. We have also improved the recognition performance of handwritten mathematical expressions (HME). Our HME recognition system is ranked 3rd in an official offline HME competition of ICFHR2020.
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Research Products
(13 results)
