TY - GEN
T1 - Reliable online stroke recovery from offline data with the data-embedding pen
AU - Liwicki, Marcus
AU - Akira, Yoshida
AU - Uchida, Seiichi
AU - Iwamura, Masakazu
AU - Omachi, Shinichiro
AU - Kise, Koichi
PY - 2011/12/2
Y1 - 2011/12/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=82355172932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82355172932&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2011.278
DO - 10.1109/ICDAR.2011.278
M3 - Conference contribution
AN - SCOPUS:82355172932
SN - 9780769545202
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1384
EP - 1388
BT - Proceedings - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
T2 - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
Y2 - 18 September 2011 through 21 September 2011
ER -