Air-writing recognition using reverse time ordered stroke context

Tsung Hsien Tsai, Jun-Wei Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A novel real-time recognition system is proposed to recognize finger air-writing characters without using any pen-starting-lift information. It presents a novel reverse time ordered stroke context to represent an air-writing trajectory in a backward way so that redundant starting-lift data can be effectively filtered out. Another two challenging problems often happen in the air-writing recognition system, i.e., the multiplicity problem of writing and the confusion problem. The first one means a character is always written differently and the second one means different various characters own similar writing trajectory. To tackle them, a three-layer hierarchical structure to represent an air-writing character with different sampling rates is proposed. All the alphabets (including lowercase, capital, and digital letters) are recognized in this system. Performance evaluation shows that the proposed solution achieves quite higher recognition accuracy (more than 94.7%) even though no starting gesture is required.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages4137-4141
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - 20 Feb 2018
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Conference

Conference24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period17/09/1720/09/17

Keywords

  • Air-writing recognition
  • Hierarchical classification
  • Reverse timeorder stroke representation

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