Fundamentals of speech recognition
Fundamentals of speech recognition
Pen Pressure Features for Writer-Independent On-Line Handwriting Recognition Based on Substroke HMM
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Context-Dependent Substroke Model for HMM-Based On-Line Handwriting Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
TreadMill Ink " Enabling Continuous Pen Input on Small Devices
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Substroke Approach to HMM-Based On-line Kanji Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Hybrid Recognition for One Stroke Style Cursive Handwriting Characters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Discriminant Substrokes for Online Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
International Journal of Applied Mathematics and Computer Science
HMM-based online handwritten gurmukhi character recognition
Machine Graphics & Vision International Journal
Writing handwritten messages on a small touchscreen
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
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This paper proposes a novel handwriting recognition interfacefor wearable computing where users write characterscontinuously without pauses on a small single writingbox. Since characters are written on the same writingarea, they are overlaid with each other. Therefore thetask is regarded as a special case of the continuous characterrecognition problem. In contrast to the conventionalcontinuous character recognition problem, location informationof strokes does not help very much in the proposedframework. To tackle the problem, substroke based hiddenMarkov models (HMMs) and a stochastic bigram languagemodel are employed. Preliminary experiments were carriedout on a dataset of 578 handwriting sequences with acharacter bigram consisting of 1,016 Japanese educationalKanji and 71 Hiragana characters. The proposed methoddemonstrated promising performance with 69.2% of hand-writingsequences beeing correctly recognized when differentstroke order was permitted, and the rate was improvedup to 88.0% when characters were written with fixed strokeorder.