在現今資訊爆炸的時代中，多媒體資訊已成為人們生活中不可或缺的一部分，影音資訊被拿來當作數位日記，成為人們紀錄資訊的一種媒介，也同時產生了大量的影像文字資訊。文字在影像中通常存在著有用的資訊，可能是路牌上的字抑或是路邊的招牌等等，因此如何讓電腦在影像中找出文字資訊並加以應用，將會是很重要的發展課題。場景文字偵測與辨識是希望透過影像處理，將影像中的文字資訊擷取出來，並藉此提供路牌提醒、自動翻譯、影像索引等各式應用，作為輔助人們的工具。
本論文提出一個快速文字偵測與辨識演算法，發展出一個能以輸入影像並得到影像中文字的系統，主要分為三大部分：(1)文字候選區域擷取、(2)文字分類與分組、(3)文字辨識。文字候選區域是以極值區域作為文字偵測器，在YCrCb及它們的反向通道等多通道上作擷取，並利用非最大值抑制將大量重複的候選區域除去，之後進行文字分類，我們使用平均局部二元模式作為特徵，並以實值自適應增強演算法訓練出分類器，分類共有兩階段，將文字分為強文字、弱文字與非文字，兩階段的分類可以同時保有高召回率與高準確率，接著再追蹤出與強文字有相似特性的弱文字，下一步進行文字分組，將個別的字母組合成單字，最後再以我們以鍊碼方向作為特徵並以支持向量機作為分類器，進行文字辨識且判斷所屬標籤，並將結果顯示在文字的上方。
實驗結果可以看出我們的系統在影片序列的測試可以達到即時的偵測速度，以及近即時的辨識速度，並對於各式字體與字的大小都可以擷取，同時可以容忍適當的轉角、模糊以及光源不均的情況，顯現出本系統的穩健性。而我們在ICDAR 2013的資料庫的文字偵測則達到了70.4%的偵測率。

In the era of information explosion, multimedia has become an indispensable part of modern life. People use videos and images as digital diary and create enormous image text data consequently. Texts in image usually contain informative data, and therefore scene text recognition system would be a promising application.
This thesis presents a fast scene text localization and recognition algorithm. We have develop a system that takes images as input and recognizes texts in the input images as output. The system consists of three parts: (1) Character candidate extraction, (2) Character classification and grouping, (3) Optical character recognition. In the first stage, extremal region(ER) is used as a candidate extractor. In order to reach high recall rate, we extract ER in multiple channels such as YCrCb and their inverted channels. A non-maximum suppression skill is introduced to eliminate overlapped candidates. In the second stage, we used mean local binary pattern as feature and train our classifier by AdaBoost. Text candidates are classified as one of strong text, weak text and non-text by a 2 stages classifier. The 2 stages classifier is intended to remain high recall and precision simultaneously. We then track the weak texts with strong texts as long as they have similar properties. Our next step was to group the candidates and transform them from character level to word level. Finally, our optical character recognition is done by using chain-code direction as feature and support vector machine as classifier.
The experimental results show that our system is able to detect text in real-time and recognize text in nearly real-time. In addition, the system can detect text in different text fonts and text size, also tolerate moderate rotation, blurring and inconsistent lighting. Thus, the robustness of the system is validated.

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