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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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