以長短期記憶模型進行筆寫單字辨識｜國立清華大學博碩士論文庫

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研究生：	卓訓發 Cho, Hsun-Fa
論文名稱：	以長短期記憶模型進行筆寫單字辨識 On-Pen Handwritten Word Recognition Using Long Short-Term Memory Model
指導教授：	周百祥 Chou, Pai H.
口試委員:	蔡明哲 Tsai, Ming-Jer 周志遠 Chou, Jerry
學位類別：	碩士 Master
系所名稱：
論文出版年：	2018
畢業學年度：	106
語文別：	英文
論文頁數：	32
中文關鍵詞：	嵌入式系統、筆寫辨識、手勢辨識、深度學習、連續資料分段、長短期記憶模型
外文關鍵詞：	embedded system, op-pen handwritten recognition, gesture recognition, deep learning, segmentation of continuous data, long short-term memory model
相關次數：	點閱：2 下載：0
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近年來，因穿戴式裝置的流行，基於動作感測的手寫辨識研究越來越多，例如空中手寫、智慧筆等等，但是能達到實用水平的應用卻很少。受限於感測器的限制、連續動作的分割難度這些問題，手寫動作辨識很難做有效率的文字輸入。在這篇論文中，我們提出了一種安裝在筆上的新型硬體裝置，搭配以長短期記憶模型(LSTM)為核心的辨識系統，讓使用者能夠用任何他們想拿來寫字的筆，作為一個有效率的文字輸入介面。

This thesis describes a system for text input from handwriting using a
conventional pen with a clip-on sensing unit. The clip-on unit is a
wireless sensor node that collects data from a triaxial accelerometer and a triaxial gyroscope and transmits it to a conventional personal computer. The host computer then performs segmentation to handle continuous handwriting, followed by LSTM-based classification. Moreover, we use a lexicon-based corrector to increase the accuracy. Experimental results show our proposed system to achieve good accuracy and reasonable latency for interactive use.

Contents i
Acknowledgments iv
Introduction 1
1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Related Work 3
1 Isolated Handwriting Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Continuous Handwriting Recognition . . . . . . . . . . . . . . . . . . . . . . . . . 4
System Overview 6
1 Pen Subsystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Host Subsystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Data Preprocessing and Segmentation 8
1 Gravity Elimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Data segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Recognition Method 15
1 Background: RNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Long Short-Term Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
i
3 Lexicon Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.1 Bayes’ Corrector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Weighted Levenshtein distance . . . . . . . . . . . . . . . . . . . . . . . . . 19
Evaluation 21
1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.1 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.2 System environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.1 Segmentation Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2 Accuarcy of Using Different Features . . . . . . . . . . . . . . . . . . . . . 24
2.3 Improvement After Lexicon Support . . . . . . . . . . . . . . . . . . . . . . 24
3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Conclusions and Future Work 28
1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
                                

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