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2021 | OriginalPaper | Chapter

GSSF: A Generative Sequence Similarity Function Based on a Seq2Seq Model for Clustering Online Handwritten Mathematical Answers

Authors : Huy Quang Ung, Cuong Tuan Nguyen, Hung Tuan Nguyen, Masaki Nakagawa

Published in: Document Analysis and Recognition – ICDAR 2021

Publisher: Springer International Publishing

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Abstract

Toward a computer-assisted marking for descriptive math questions, this paper presents clustering of online handwritten mathematical expressions (OnHMEs) to help human markers to mark them efficiently and reliably. We propose a generative sequence similarity function for computing a similarity score of two OnHMEs based on a sequence-to-sequence OnHME recognizer. Each OnHME is represented by a similarity-based representation (SbR) vector. The SbR matrix is inputted to the k-means algorithm for clustering OnHMEs. Experiments are conducted on an answer dataset (Dset_Mix) of 200 OnHMEs mixed of real patterns and synthesized patterns for each of 10 questions and a real online handwritten mathematical answer dataset of 122 student answers at most for each of 15 questions (NIER_CBT). The best clustering results achieved around 0.916 and 0.915 for purity, and around 0.556 and 0.702 for the marking cost on Dset_Mix and NIER_CBT, respectively. Our method currently outperforms the previous methods for clustering HMEs.

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Metadata
Title
GSSF: A Generative Sequence Similarity Function Based on a Seq2Seq Model for Clustering Online Handwritten Mathematical Answers
Authors
Huy Quang Ung
Cuong Tuan Nguyen
Hung Tuan Nguyen
Masaki Nakagawa
Copyright Year
2021
Book
Document Analysis and Recognition – ICDAR 2021

Print ISBN: 978-3-030-86330-2

Electronic ISBN: 978-3-030-86331-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-86331-9_10

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