What is the Reward for Handwriting?-A Handwriting Generation Model Based on Imitation Learning

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Analyzing the handwriting generation process is an important issue and has been tackled by various generation models, such as kinematics based models and stochastic models. In this study, we use a reinforcement learning (RL) framework to realize handwriting generation with the careful future planning ability. In fact, the handwriting process of human beings is also supported by their future planning ability; for example, the ability is necessary to generate a closed trajectory like '0' because any shortsighted model, such as a Markovian model, cannot generate it. For the algorithm, we employ generative adversarial imitation learning (GAIL). Typical RL algorithms require the manual definition of the reward function, which is very crucial to control the generation process. In contrast, GAIL trains the reward function along with the other modules of the framework. In other words, through GAIL, we can understand the reward of the handwriting generation process from handwriting examples. Our experimental results qualitatively and quantitatively show that the learned reward catches the trends in handwriting generation and thus GAIL is well suited for the acquisition of handwriting behavior.

本文言語英語
ホスト出版物のタイトルProceedings - 2020 17th International Conference on Frontiers in Handwriting Recognition, ICFHR 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ109-114
ページ数6
ISBN(電子版)9781728199665
DOI
出版ステータス出版済み - 9 2020
イベント17th International Conference on Frontiers in Handwriting Recognition, ICFHR 2020 - Dortmund, ドイツ
継続期間: 9 7 20209 10 2020

出版物シリーズ

名前Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
2020-September
ISSN(印刷版)2167-6445
ISSN(電子版)2167-6453

会議

会議17th International Conference on Frontiers in Handwriting Recognition, ICFHR 2020
国/地域ドイツ
CityDortmund
Period9/7/209/10/20

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識

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