Vision Based Fingertip Input System by Recognizing Mandarin Phonetic Symbol Combinations
碩士 === 國立中央大學 === 資訊工程研究所 === 99 === This paper proposes a vision-based handwritten Mandarin phonetic symbols 1(MPS1, Bopomofo) combinations recognition system. This system uses web camera as the input device to detect the trajectory of user’s fingertip in order to recognize user’s handwritten Manda...
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ndltd-TW-099NCU053920472017-07-13T04:20:33Z http://ndltd.ncl.edu.tw/handle/79238618527413016973 Vision Based Fingertip Input System by Recognizing Mandarin Phonetic Symbol Combinations 基於視覺的手寫軌跡注音符號組合辨識系統 Qun-Jing Shen 沈群景 碩士 國立中央大學 資訊工程研究所 99 This paper proposes a vision-based handwritten Mandarin phonetic symbols 1(MPS1, Bopomofo) combinations recognition system. This system uses web camera as the input device to detect the trajectory of user’s fingertip in order to recognize user’s handwritten Mandarin phonetic symbols combinations. First, the system locates the fingertip by considering the farthest point from center of palm and records every frame to form the fingertip trajectory. Second, we remove partial entering strokes and leaving strokes by using a bounding box which we propose in this paper. Afterwards, the preprocessed trajectory is encoded by 8-chain codes. Then we remove the rest of entering strokes, leaving strokes, and jitter strokes, and find the turning point of 8-chain codes. Third, we extract features to classify the number of MPS1 combinations by using a Naive Bayes classifier. Afterwards, we separate the MPS1 combinations into single MPS1 symbols and use Hidden Markov models (HMM) to recognize each single MPS1 symbol in the combinations. Finally, we combine the above HMM results with the Naive Bayes classifier’s result to recognize these MPS1symbol combinations. HSU-YUNG CHENG 鄭旭詠 2011 學位論文 ; thesis 54 zh-TW |
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碩士 === 國立中央大學 === 資訊工程研究所 === 99 === This paper proposes a vision-based handwritten Mandarin phonetic symbols 1(MPS1, Bopomofo) combinations recognition system. This system uses web camera as the input device to detect the trajectory of user’s fingertip in order to recognize user’s handwritten Mandarin phonetic symbols combinations.
First, the system locates the fingertip by considering the farthest point from center of palm and records every frame to form the fingertip trajectory. Second, we remove partial entering strokes and leaving strokes by using a bounding box which we propose in this paper. Afterwards, the preprocessed trajectory is encoded by 8-chain codes. Then we remove the rest of entering strokes, leaving strokes, and jitter strokes, and find the turning point of 8-chain codes. Third, we extract features to classify the number of MPS1 combinations by using a Naive Bayes classifier.
Afterwards, we separate the MPS1 combinations into single MPS1 symbols and use Hidden Markov models (HMM) to recognize each single MPS1 symbol in the combinations. Finally, we combine the above HMM results with the Naive Bayes classifier’s result to recognize these MPS1symbol combinations.
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HSU-YUNG CHENG |
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HSU-YUNG CHENG Qun-Jing Shen 沈群景 |
author |
Qun-Jing Shen 沈群景 |
spellingShingle |
Qun-Jing Shen 沈群景 Vision Based Fingertip Input System by Recognizing Mandarin Phonetic Symbol Combinations |
author_sort |
Qun-Jing Shen |
title |
Vision Based Fingertip Input System by Recognizing Mandarin Phonetic Symbol Combinations |
title_short |
Vision Based Fingertip Input System by Recognizing Mandarin Phonetic Symbol Combinations |
title_full |
Vision Based Fingertip Input System by Recognizing Mandarin Phonetic Symbol Combinations |
title_fullStr |
Vision Based Fingertip Input System by Recognizing Mandarin Phonetic Symbol Combinations |
title_full_unstemmed |
Vision Based Fingertip Input System by Recognizing Mandarin Phonetic Symbol Combinations |
title_sort |
vision based fingertip input system by recognizing mandarin phonetic symbol combinations |
publishDate |
2011 |
url |
http://ndltd.ncl.edu.tw/handle/79238618527413016973 |
work_keys_str_mv |
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1718495362403008512 |