In present daily life text plays an important role in daily life because of its rich information that is why automatic text detection in natural images has many applications. But detecting the text from natural image is always a challenging problem. Due to the presence of variation in the background and as the size of the text also not fixed in case of natural images it is very difficult to identify the text accurately. Through tremendous efforts have recently been devoted in this research but still reading texts in unconstrained environment is still challenging and remain a problem. Today text detection finds many applications in various fields, including visual impairment assistance, tourist assistance, content based image retrieval and unmanned ground vehicle navigation. Today most of the images are taken from the camera and other handhold devices which are not fixed and sometimes due to the movement of the object the problem of blurring is observed which makes it even more difficult to detect the text from natural images. Here in this thesis an idea is proposed to detect and recognize the text contains in the image as the main problem in Computer vision is to separate the text from the background components. There are many methods which are still used to detect the text from the natural scene such as text detection using edge analysis, robust text detection, Real time text tracking, but none of them is promising. In this paper text detection is carried by using canny edge detection algorithm and MSER based method along with the image enhancement which results in the improved performance in terms of text detection. In addition, we improve current MSERs by developing a contrast Enhancement mechanism that enhances region stability of text patterns.to remove the blurring caused during the capture of image Lucy Richardson de blurring Algorithm is used.
Text Detection, Scene Test, MSER, OCR
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