摘要
自動地對影片中的文字做偵測，對於影片的檢索和瞭解是不可或缺的工作。 而本論文主要是針對輔助駕駛系統做應用，利用自動對高速公路牌（指示牌、速限牌）上的文字做偵測，提供駕駛者在高速公路上的導航，例如：現在駕駛者所處的位置、方向、現在應該保持的速度。 讓駕駛者可以專心地開車，而不會因為為了看道路上的路牌而造成駕駛不當，或者因為超速而造成車禍； 且為了響應2008年北京奧運，在未來工作方面，除了對高速公路牌上的文字自動辨識之外，可以進階地將所攝影機擷取到的文字，自動地翻譯成各國語言，使各國來的人士能容易地了解各個路牌所代表的意思。 本論文提出一個對擷取道路牌上強而有效的新特徵，使用顏色的資訊來對高速公路上的道路牌做定位，並結合邊的資訊，自動偵測道路牌上的文字。 主要的架構可分為三個，第一個步驟，利用顏色和不同物質具有不同傳導係數的特性，配合類神經網路的訓練，將路牌和其他的物質分開； 第二個步驟，加入仿射（affine）矯正，將照相機在不適當的拍攝角度，所拍得的變形道路牌復原回來，使道路牌上的文字正對著相機，增加框選文字的正確性； 第三個步驟，利用 Canny 邊緣偵測來取得邊的資訊，藉此在每一個道路牌上框出文字的候選區。 在實驗的部分，我們希望本論文提出的方法能適用在大多的情況下，所以從多段影片之中擷取出20段影片，其中包括了晴天、多雲和筆直的或彎曲的道路情況。在道路牌定位的部份，檢出率(recall)和精確率(precision)分別為 91.1%及 80.8%，在文字偵測上則是 93.6% 和 88.0%。
並列摘要
With the advancement of scientific technologies, the cost of digital camera decreases rapidly. It is a trend to improve and uplift the living quality of people using image processing techniques. Automatic detection of text from video is one of the applications which is an essential task for understanding and indexing of video. In this thesis, a driver assistant system is designed by automatic detection of text on road sign (guid signs and limit signs) on highway to provide drivers information for navigation, such as location, direction, and speed limit. It may also alleviate the load of driver who may lose his/her attention looking at road signs while focusing on driving. For 2008 Olympic in Beijing, there will be many foreigner visiting China and not all of them understand Chinese language. Hence, the translation of text on road sign is another goal that can be accomplished. In this thesis, a set of feature is devised to detect road signs. The proposed system consists of three modules. The first module finds the constituting colors of road signs using the color transform model and locates road sign candidates. In the second module, affine transformation is performed to restore road signs which are captured by camera in different positions to let every road sign seems to be vertical to the camera optical axis. Moreover, affine transformation can improve the accuracy in detecting texts embedded in road signs. As to the third module, it performs the task of detecting texts on road signs. The method we adopt is canny edge detector to obtain clearer edge information. Experiments were conducted on a variety of situations. 20 video sequences (sunny*10 and cloudy*10) including light variations and straight or cursive road conditions were tested to verify the validity of the proposed method. The recall and precision rates in locating road sign are 91.1% and 80.8%, respectively. The recall and precision rates in detection text are 93.6% and 88.0%.