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研究生(外文):Hsin-Ying Wang
論文名稱(外文):In-air Handwriting Chinese Character Recognition base on LSTM
指導教授(外文):Kuo-Chin FanJun-Wei Hsieh
外文關鍵詞:In-air Handwriting Chinese CharacterLong Short-Term MemoryCharacter Recognition
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基於以上特點,本文提出使用時間遞迴神經網路(RNN)家族中的長短期記憶(Long Short-Term Memory, LSTM)模型作為辨識的核心架構。因深度學習需要龐大的訓練資料,雖簡體字資料庫在中國大陸已有許多單位投入並建立資料庫,但並不符合國人撰寫習慣,且繁體字並無相關開放式資料庫,所以本論文自行收集了492個繁體字,總計共2萬多筆資料,將資料透過預處理提取筆劃轉折點,而為符合LSTM需固定時序的特性,本文將筆劃切成多種固定的數量,並利用形狀上下文(Shape Context)統計空間分布特徵作為辨識模型的輸入,透過實驗設計藉由中文字的筆劃數增減及設定不同維度的形狀上下文進行準確度及穩定度的測試中,實驗的結果可得本文之中文空中手寫辨識準確度達到98.6%。
In the process of human information communication, reading and writing are the most basic skills, and characters are the one of important parts. Therefore, in the automated handwriting recognition system, Chinese characters are more complicated and have a larger number of common words than English characters and numerals. Chinese characters possess the difference of font structure and stroke sequence. In-air handwriting scenario, each the Chinese characters has both features of real stroke and virtual stroke, and the presentation is different from handwriting on paper. Handwriting on paper only appears the real stroke, the virtual stroke couldn’t show on paper or screen because of lifting pen. However, when users handwrite in the air, the process of stroke is continuous and one stroke-finished; it makes the in-air handwriting own two characteristics: real and virtual strokes and time sequence.
Based on the above characteristics, this paper proposes to use the Long Short-Term Memory (LSTM) model as the core model for recognition. Deep learning requires a lot of training data. Although there are many institutions in China which devote to establish the Simplified Chinese dataset, it doesn’t fit the Taiwanese habit. Therefore, we collect 492 Traditional Chinese characters, about more than 20,000 data. To extract the turning point of the stroke through the preprocessing. In order to conform the characteristic of LSTM which fixed timing, the stroke is cut many fixed quantities, and been the input of recognition model by using shape context statistical spatial distribution feature. This paper test accuracy and stability by increasing and decreasing of strokes and setting the shape context of different dimensions. According experiments, the accuracy of recognizing Chinese characters is 98.6%.
摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1 研究背景及動機 1
1.2 研究目的 1
1.3 論文架構 2
第二章 相關文獻探討 3
2.1 手寫辨識 3
2.2 人機互動 (HCI) 4
2.3 形狀上下文 (Shape Context) 5
2.4 遞迴神經網路 (RNN) 6
2.5 長短期記憶 (LSTM) 8
2.6 激活函數 11
2.7 批次正規化(Batch Normalization) 12
2.8 丟棄法(Dropout) 12
第三章 中文字手寫辨識系統 13
3.1 系統架構 13
3.2 資料收集 14
3.3 前處理 15
3.3.1 刪除重疊點、角點偵測 15
3.3.2 軌跡正規化、筆劃切割 18
3.3.3 形狀上下文 19
3.4 辨識模型 20
第四章 實驗結果與討論 22
4.1 實驗環境 22
4.2 訓練、測試資料庫 22
4.3 實驗設計與結果分析 29
第五章 結論與未來展望 38
5.1 結論 38
5.2 未來展望 39
參考文獻 40
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[17] 鄒佩珊, “空中手寫中文字辨識,” 國立中央大學 資訊工程學系碩士論文, 2018.
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