以外形特徵為基礎之影像語言分類器-應用於破碎中文字合併

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范聖恩(Sheng-En Fann) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以外形特徵為基礎之影像語言分類器-應用於破碎中文字合併
(Image Language Identification Using Shapelet Feature-Application in Merging Broken Chinese Characters)

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摘要(中)

本論文使用外形特徵(Shapelet Feature)搭配Adaboost與 SVM兩種機器學習演算法來建構影像語言分類器。不同於過去，從上而下的概念將整張文件影像、或是某個文章、段落進行語言種類判別，本論文使用機器學習的方式自動計算足以分辨語言種類的特徵，可以細膩快速的判定文件中每個連通物件的語言種類(中文或是英文)。
輸入文字影像首先被邏輯上分成若干個區域，並計算各區域影像內四個方向的灰階梯度資訊以建構低階特徵，再將低階特徵傳入區域分類器計算其外形特徵，最後將各區域的區域外形特徵集合起來(全域外形特徵)即形成最終語言分類器的輸入特徵。
因為考量繁體中文字結構上的特性，對於文件中判定為中文部首、中文部分字的連通物件，我們再嘗試將其與左右連通物件合併以形成完整中文字。實驗除了分別比較兩階段Adaboost與Adaboost + SVM訓練方式效果的優劣外，亦將語言分類器發揮在以可攜式攝影器材取像的應用上。結果證明，本論文提出的方法可以實際應用在現今多語言文件的分析，除了能有效幫助後端文字辨識正確率的提升與文件內容的擷取，也能在不具備其它語系相關知識下，將此方法推廣至其它語系的語言分類上。

摘要(英)

In this paper, a novel language identifier using shapelet feature with Adaboost and SVM has been developed. Different from previous works, our proposed mechanism not only can identify the language type in either Chinese or English of each connected component in the document image, but also obtain better robustness and gain highly efficiency and performance.
First of all, the input connected component image has been divided into several sub-windows logically. After then, the gradient responses of each sub-image in different directions are extracted and the local average of these responses around each pixel is manipulated. In the following, the Adaboost is performed to select a subset of its low-level features to construct a mid-level shapelet feature. Finally, the shapelet features are merged together in all sub-windows. Through the above process, all of the information from different parts of the image is combined together and treated as the feature of the final language identifier.
The broken or partial Chinese character connected components are tried to be combined with their neighboring connected components. The experimental results demonstrate that our proposed method not only can achieve the goal of improving the correctness rate for OCR process, but also obtain great merits for advanced document analysis.

關鍵字(中)

★ 機器學習
★ 影像語言分類
★ 影像語言辨識
★ 影像處理
★ 外形特徵

關鍵字(英)

★ image language identification
★ machine learning
★ shapelet feature
★ image language classification
★ image processing

論文目次

中文摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章導論 1
1.1 研究動機與目的 1
1.2系統架構 5
1.3 論文架構 6
第二章文獻探討相關研究 7
2.1 以文件為基礎語言分類 7
2.2 以文字為基礎語言分類 11
第三章相關演算法 16
3.1 Adaboost演算法 16
3.2 SVM演算法 20
3.2.1 Soft margin 24
3.2.2 Kernel method 25
3.3 K-Means演算法 27
3.3.1 群集中心初始演算法( CCIA) 29
3.3.2 以密度為基礎多重規模資料壓縮演算法( DBMSDC) 31
第四章前處理 33
4.1 二值化 35
4.2 連通物件分析 36
4.2.1 4-連通物件擷取 38
4.2.2 8-連通物件擷取 38
4.3 雜訊去除與圖文分離 39
4.4 文字行偵測 40
4.5 連通物件合併 43
4.6 文字部件區域標定 44
第五章特徵抽取與訓練 50
5.1 特徵抽取 53
5.1.1 低階特徵抽取 54
5.1.2 區域外形特徵抽取 56
5.1.3 最終語言分類器 58
5.2 討論 60
第六章實驗結果 61
6.1 Adaboost + SVM與兩階段Adaboost分類正確率比較 66
6.2 Adaboost + SVM與兩階段Adaboost分類時間比較 71
6.3 語言分類應用於中文名片辨識 75
第七章結論與未來工作 82
參考文獻 84

參考文獻

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指導教授

范國清、溫敏淦
(Kuo-Chin Fan、Ming-Gang Wen)

審核日期

2009-2-2

推文