Which is the best way to detect object in this case?

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The keypoint-based features are typically used for feature extraction in images containing natural context for detecting a fast matching (e.g. for computing a homography-matrix). So these methods are not suitable in your case since you don't have much structure in your images. You could detect the logos properly but not the lines. The same basically holds for LBP, HOG, HAAR-like. These features are typically used for describing texture, e.g. used for segmentation but also used in learning approaches. However these don't seem to be useful either for your application. Or is this 'winter'-logo always present in your images? Then in theory you could use a cascade classifier using one of the 'texture'-features to detect that. But honestly, this would be overkill!!

If I got you right, then you have the title block in advance and only the drawing may be rotated but actually you don't care about the drawings at all. Ideas:

1. Detect the title-block by hierarchical template-matching (see e.g. http://opencv-code.com/tutorials/fast-template-matching-with-image-pyramid/). If you don't have the complete title-block but just the logo or parts of it you can also go for this approach, since you'll get the best match

2. If you don't have the title block beforehand or would like to get first the rough location: Detect the two long lines bordering your title-block using cv::HoughLines or cv::HoughLinesP (http://docs.opencv.org/modules/imgproc/doc/feature_detection.html?highlight=houghlines#void%20HoughLinesP%28InputArray%20image,%20OutputArray%20lines,%20double%20rho,%20double%20theta,%20int%20threshold,%20double%20minLineLength,%20double%20maxLineGap%29).

The detected region you can then pass to tesseract (http://code.google.com/p/tesseract-ocr/), an open-source text-recognition engine which will give you the text which you could then match with your words ('Approved by', etc.). Good luck!