#text detection

the main aim of this paper is to aid the visually impaired people with object detection and text detection using deep learning. Object detection is done using a convolution neural network and text recognition is done by optical character recognition. The detected output is converted into speech using text to the speech synthesizer. Object detection comprises of two methods. One is object localization and the other is image classification. Image classification refers to the prediction of classes of different objects within an image. Object localization infers the location of objects using bounding boxes. 

Fine-grained classification using recognized scene text in natural images. In this we extract the text from the image and the extracted text is translated to user known language by using language translator. We apply this method in military services. In this the users create their account by giving their details. Now, the user have their user name and password for their further process. The user sends the image to the end user in encrypted type and they can send document also. Encryption is performed by using RSA algorithm. Now, the end user receive the image and they view the image in decrypted type. The end user extract the text from image. The extraction is performed by using OCR algorithm. We subtract the background by background filtering. Once text regions are detected, it perform text recognition. We used two methods for extraction i.e., character extractor and line extractor. The character extractor generates the bounding boxes of words. Each character is compared with ASCII code for translation. In line extractor, it extracts line by line in the image. The extracted text is translated to user known language by using language translator. The accuracy obtained was 85 to 90 percent. 

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.