臺灣博碩士論文加值系統

從影像或視訊中偵測出文字，有非常的廣泛的應用價值。例如，事先準確地偵測出文字所在的位置，則後續的文字辨識系統可淋漓盡致的發揮其效能。對車牌辨識而言，準確地偵測出車牌文字所在，是其成功辨識的關鍵所在。就廣告設計而言，文字偵測的結果，有利其進行廣告詞或廣告背景的替換。就以內容為基礎的檢索系統，文字的偵測有利其完成結合文字與影像內容之檢索。
本研究以支持向量機中的「非線性支持向量機」作為文字偵測的分類器，而最佳化的支持向量機在特徵選取與資料庫建立的決定上，是具有難度的。由於文字的種類，因國別、字型、大小、變形等等因素而有所差異，因此如何準確的偵測出文字所在位置，是具有挑戰性的研究的主題。本論文採用「支持向量機」來針對文字的特徵進行分類的工作，透過文字結構所具有的特徵差異，達到高準確的分類能力。由實驗數據顯示，利用「支持向量機」訓練出來的文字分類系統，有極高的文字偵測正確率。

Detecting text from image or video streams can be widely used in a variety of application fields. For example, detecting and extracting text from documents in advance can facilitate following OCR module. For automatic recognition of vehicle license plate, the key point is to locate the position of vehicle license plate successfully. For advertisement design, it is easy to replace text or background if text regions can be automatically extracted. For content-based retrieval, automatic extraction of text regions can lead more efficient and effective retrieval results since text and content can be incorporated to facilitate retrieval process.
In this thesis, a new method to detect text using support vector machine (SVM) is proposed. The challenges of this approach include the following two issues. First, feature selection and database construction are two essential processes to achieve optimal SVM. However, it is hard to design the two tasks. Second, texts are often embedded in images and may vary in language, font, size, and deformation, which, in turn enhance the difficulty of the text detection problem. In this thesis, discriminating features are adopted and bootstrap training are involved to construct training database for text detection using SVM. Experimental results prove the effectiveness of the proposed text detection method.

摘要 i
Abstract ii
誌謝 iv
目錄 v
圖目錄 vii
第一章 序論 1
1.1 研究背景與動機 1
1.2 問題描述 2
1.2.1 文字大小之變異性 3
1.2.2 背景之色彩差異性 3
1.2.3 非文字特徵誤判所產生的雜訊 4
1.3 相關研究 5
1.4 論文架構 8
第二章 基本觀念 10
2.1 SVM分類器的介紹 10
2.2 非線性SVM分類器的設計原理 18
第三章 影像資料庫前處理 23
3.1 影像資料的種類與來源 23
3.2 RGB色彩空間特徵轉為Gray灰階亮度特徵 23
3.3 色彩空間轉換與特徵擷取之相關實驗數據 25
第四章 支持向量機進行特徵之分類 28
4.1 支持向量機於本論文的應用 28
4.2 文字資料庫之建立 28
4.3 非文字資料庫之建立－靴量(Bootstrap) 33
4.4 文字支持向量機執行結果 37
第五章 金字塔影像層之熔合處理 39
5.1文字區域矩形之決定 39
5.2 金字塔影像層熔合處理 45
5.3文字區塊之擷取 46
第六章 實驗與測試結果 52
6.1 實作環境與設備 52
6.2 實驗結果 53
6.3 實驗結果比較 56
6.4 辨識錯誤分析 57
第七章 結論與未來研究 62
7.1 論文成果 62
7.2 未來研究 62

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