| 研究課題/領域番号 |
18K18068
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| 研究機関 | 東京農工大学 |
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研究代表者 |
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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| 研究期間 (年度) |
2018-04-01 – 2021-03-31
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| キーワード | clustering / weakly supervised / hierarchical features / CNN |
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| 研究実績の概要 |
We proposed a CNN based method to learn both localization and classification representations of mathematical symbols in handwritten formula images. Symbols in various scales are located and classified by multi-level features of multi-scaled CNN. We train the CNN networks by weakly supervised training and fine-tune them by symbols attention to enhance classification and location prediction. Multi-level spatial representations are extracted from the CNN for calculating the distance. Experiments on our collected datasets and the CROHME dataset show the promising results.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We have proposed a learning model for clustering offline handwritten mathematical expression.
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| 今後の研究の推進方策 |
We continue to develop the method for clustering online handwritten mathematical expression. We focus on weakly supervised learning method and hierarchical representation.
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| 次年度使用額が生じた理由 |
We are going to use the budget for attending international conferences, revising and publishing journal papers.
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