I want to decide the language of the text image based on the Hor. projection. I computed the Hor projection as shown in the following figures. So how can I find that if the image has an extreme horizontal peak (such as in figure 1 and 2), so the text belongs to Language X and if the image has more than one peaks (such as in figure 3 and 4), so it belongs to language Y.
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Counting the number of peaks, and using that to infer written language seems a very broad brush to apply to the problem. As well, it is a difficult thing to measure, since on some samples there may easily be a second peak. Looking at the second figure, I see it might easily lead you astray.
Anyway, there are simpler methods that might not be so easily led astray, that are trivial to compute. For example, compute a normalized area under that curve, when viewed as x(y). Thus, viewing x as the independent variable, compute the area of the curve as trapz(x), then divide that result by max(x).
measure = trapz(x)/max(x);
The point is, figures 1 and 2 have relatively little area under that curve, relative to the maximum value that x attains. Whereas figures 3 and 4 will show a seriously different result from the above trivial computation.
I'm not sure how the above curves are defined, so you might gain the same information from a tool like polyarea, rather than trapz. And since I don't know if the points on the curve are equally spaced, it is hard to be sure how exactly to compute that result. But you should get the general idea.
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