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On-Pen Handwritten Word Recognition Using Long Short-Term Memory Model
Thesis

On-Pen Handwritten Word Recognition Using Long Short-Term Memory Model

Cho, Hsun-Fa
Masters, 國立清華大學, 資訊工程學系所
2017

Abstract

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 embedded system op-pen handwritten recognition gesture recognition deep learning segmentation of continuous data long short-term memory model
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.

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