The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Un cadre de fusion entre CNN et RNN est proposé spécifiquement pour la reconnaissance de l'écriture aérienne. En modélisant l'écriture aérienne à l'aide de caractéristiques spatiales et temporelles, le réseau proposé peut apprendre plus d'informations que les techniques existantes. Les performances du réseau proposé sont évaluées à l'aide des ensembles de données alphabétiques et numériques de la base de données publique, à savoir le 6DMG. La précision moyenne du réseau de fusion proposé surpasse les autres techniques, c'est-à-dire que 99.25 % et 99.83 % sont observés respectivement dans le geste alphabétique et le geste numérique. Une structure simplifiée du RNN est également proposée, qui peut atteindre une vitesse environ deux fois supérieure à celle du réseau BLSTM ordinaire. Il est également confirmé que seule la distance entre des points d'échantillonnage consécutifs est suffisante pour atteindre des performances de reconnaissance élevées.
Buntueng YANA
Osaka University
Takao ONOYE
Osaka University
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Buntueng YANA, Takao ONOYE, "Air-Writing Recognition Based on Fusion Network for Learning Spatial and Temporal Features" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 11, pp. 1737-1744, November 2018, doi: 10.1587/transfun.E101.A.1737.
Abstract: A fusion framework between CNN and RNN is proposed dedicatedly for air-writing recognition. By modeling the air-writing using both spatial and temporal features, the proposed network can learn more information than existing techniques. Performance of the proposed network is evaluated by using the alphabet and numeric datasets in the public database namely the 6DMG. Average accuracy of the proposed fusion network outperforms other techniques, i.e. 99.25% and 99.83% are observed in the alphabet gesture and the numeric gesture, respectively. Simplified structure of RNN is also proposed, which can attain about two folds speed-up of ordinary BLSTM network. It is also confirmed that only the distance between consecutive sampling points is enough to attain high recognition performance.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1737/_p
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@ARTICLE{e101-a_11_1737,
author={Buntueng YANA, Takao ONOYE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Air-Writing Recognition Based on Fusion Network for Learning Spatial and Temporal Features},
year={2018},
volume={E101-A},
number={11},
pages={1737-1744},
abstract={A fusion framework between CNN and RNN is proposed dedicatedly for air-writing recognition. By modeling the air-writing using both spatial and temporal features, the proposed network can learn more information than existing techniques. Performance of the proposed network is evaluated by using the alphabet and numeric datasets in the public database namely the 6DMG. Average accuracy of the proposed fusion network outperforms other techniques, i.e. 99.25% and 99.83% are observed in the alphabet gesture and the numeric gesture, respectively. Simplified structure of RNN is also proposed, which can attain about two folds speed-up of ordinary BLSTM network. It is also confirmed that only the distance between consecutive sampling points is enough to attain high recognition performance.},
keywords={},
doi={10.1587/transfun.E101.A.1737},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Air-Writing Recognition Based on Fusion Network for Learning Spatial and Temporal Features
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1737
EP - 1744
AU - Buntueng YANA
AU - Takao ONOYE
PY - 2018
DO - 10.1587/transfun.E101.A.1737
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2018
AB - A fusion framework between CNN and RNN is proposed dedicatedly for air-writing recognition. By modeling the air-writing using both spatial and temporal features, the proposed network can learn more information than existing techniques. Performance of the proposed network is evaluated by using the alphabet and numeric datasets in the public database namely the 6DMG. Average accuracy of the proposed fusion network outperforms other techniques, i.e. 99.25% and 99.83% are observed in the alphabet gesture and the numeric gesture, respectively. Simplified structure of RNN is also proposed, which can attain about two folds speed-up of ordinary BLSTM network. It is also confirmed that only the distance between consecutive sampling points is enough to attain high recognition performance.
ER -