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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 17th International Conference on Frontiers in Handwriting Recognition, ICFHR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages109-114
Number of pages6
ISBN (Electronic)9781728199665
DOIs
Publication statusPublished - Sep 2020
Event17th International Conference on Frontiers in Handwriting Recognition, ICFHR 2020 - Dortmund, Germany
Duration: Sep 7 2020Sep 10 2020

Publication series

NameProceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
Volume2020-September
ISSN (Print)2167-6445
ISSN (Electronic)2167-6453

Conference

Conference17th International Conference on Frontiers in Handwriting Recognition, ICFHR 2020
CountryGermany
CityDortmund
Period9/7/209/10/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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