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As the others proposed finding the horizontal and vertical lines seems to be a nice way to go. Below you can find such a solution. In case you have any question feel free to ask, though I have added comments through my code so it should not be hard to follow.

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
    // Load source image
    string filename = "table.jpg";
    Mat src = imread(filename);

    // Check if image is loaded fine
        cerr << "Problem loading image!!!" << endl;

//    // Show source image
//    imshow("src", src);

    // resizing for practical reasons
    Mat rsz;
    Size size(800, 900);
    resize(src, rsz, size);

    imshow("rsz", rsz);

    // Transform source image to gray if it is not
    Mat gray;

    if (rsz.channels() == 3)
        cvtColor(rsz, gray, CV_BGR2GRAY);
        gray = rsz;

    // Show gray image
    imshow("gray", gray);

    // Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol
    Mat bw;
    adaptiveThreshold(~gray, bw, 255, CV_ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 15, -2);

    // Show binary image
    imshow("binary", bw);

image description

    // Create the images that will use to extract the horizonta and vertical lines
    Mat horizontal = bw.clone();
    Mat vertical = bw.clone();

    int scale = 15; // play with this variable in order to increase/decrease the amount of lines to be detected

    // Specify size on horizontal axis
    int horizontalsize = horizontal.cols / scale;

    // Create structure element for extracting horizontal lines through morphology operations
    Mat horizontalStructure = getStructuringElement(MORPH_RECT, Size(horizontalsize,1));

    // Apply morphology operations
    erode(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1));
//    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1)); // expand horizontal lines

    // Show extracted horizontal lines
    imshow("horizontal", horizontal);

image description

    // Specify size on vertical axis
    int verticalsize = vertical.rows / scale;

    // Create structure element for extracting vertical lines through morphology operations
    Mat verticalStructure = getStructuringElement(MORPH_RECT, Size( 1,verticalsize));

    // Apply morphology operations
    erode(vertical, vertical, verticalStructure, Point(-1, -1));
    dilate(vertical, vertical, verticalStructure, Point(-1, -1));
//    dilate(vertical, vertical, verticalStructure, Point(-1, -1)); // expand vertical lines

    // Show extracted vertical lines
    imshow("vertical", vertical);

image description

    // create a mask which includes the tables
    Mat mask = horizontal + vertical;
    imshow("mask", mask);

image description

    // find the joints between the lines of the tables, we will use this information in order to descriminate tables from pictures (tables will contain more than 4 joints while a picture only 4 (i.e. at the corners))
    Mat joints;
    bitwise_and(horizontal, vertical, joints);
    imshow("joints", joints);

image description

    // Find external contours from the mask, which most probably will belong to tables or to images
    vector<Vec4i> hierarchy;
    std::vector<std::vector<cv::Point> > contours;
    cv::findContours(mask, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

    vector<vector<Point> > contours_poly( contours.size() );
    vector<Rect> boundRect( contours.size() );
    vector<Mat> rois;

    for (size_t i = 0; i < contours.size(); i++)
        // find the area of each contour
        double area = contourArea(contours[i]);

//        // filter individual lines of blobs that might exist and they do not represent a table
        if(area < 100) // value is randomly chosen, you will need to find that by yourself with trial and error procedure

        approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
        boundRect[i] = boundingRect( Mat(contours_poly[i]) );

        // find the number of joints that each table has
        Mat roi = joints(boundRect[i]);

        vector<vector<Point> > joints_contours;
        findContours(roi, joints_contours, RETR_CCOMP, CHAIN_APPROX_SIMPLE);

        // if the number is not more than 5 then most likely it not a table
        if(joints_contours.size() <= 4)


//        drawContours( rsz, contours, i, Scalar(0, 0, 255), CV_FILLED, 8, vector<Vec4i>(), 0, Point() );
        rectangle( rsz, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 0), 1, 8, 0 );

    for(size_t i = 0; i < rois.size(); ++i)
        /* Now you can do whatever post process you want
         * with the data within the rectangles/tables. */
        imshow("roi", rois[i]);

image description image description

    imshow("contours", rsz);

image description

    return 0;

Of course you will need to try it by yourself and apply any modifications that might be needed depending on your dataset. Enjoy ;-).