Reliable online stroke recovery from offline data with the data-embedding pen

Marcus Liwicki, Yoshida Akira, Seiichi Uchida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

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

7 Citations (Scopus)

Abstract

In this paper we propose a complete system for online stroke recovery from offline data. The key idea of our approach is to use a novel pen device which is able to embed meta information into the ink during writing the strokes. This pen-device overcomes the need to get access to any memory on the pen when trying to recover the information, which is especially useful in multi-writer or multi-pen scenarios. The actual data-embedding is achieved by an additional ink dot sequence along a handwritten pattern during writing. We design the ink-dot sequence in such a way that it is possible to retrieve the writing direction from a scanned image. Furthermore, we propose novel processing steps in order to retrieve the original writing direction and finally the embedded data. In our experiments we show that we can reliably recover the writing direction of various patterns. Our system is able to determine the writing direction of straight lines, simple patterns with crossings (e.g., "x" and "II"), and even more complex patterns like handwritten words and symbols.

Original languageEnglish
Title of host publicationProceedings - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
Pages1384-1388
Number of pages5
DOIs
Publication statusPublished - 2011
Event11th International Conference on Document Analysis and Recognition, ICDAR 2011 - Beijing, China
Duration: 2011 Sep 182011 Sep 21

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363

Other

Other11th International Conference on Document Analysis and Recognition, ICDAR 2011
CountryChina
CityBeijing
Period11/9/1811/9/21

Keywords

  • data-embedding pen
  • information encoding
  • stroke recovery

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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