Alexander Whatley Alexander Whatley - 2 years ago 210
Python Question

Split text lines in scanned document

I am trying to find a way to break the split the lines of text in a scanned document that has been adaptive thresholded. Right now, I am storing the pixel values of the document as unsigned ints from 0 to 255, and I am taking the average of the pixels in each line, and I split the lines into ranges based on whether the average of the pixels values is larger than 250, and then I take the median of each range of lines for which this holds. However, this methods sometimes fails, as there can be black splotches on the image.

Is there a more noise-resistant way to do this task?

EDIT: Here is some code. "warped" is the name of the original image, "cuts" is where I want to split the image.

warped = threshold_adaptive(warped, 250, offset = 10)
warped = warped.astype("uint8") * 255

# get areas where we can split image on whitespace to make OCR more accurate
color_level = np.array([np.sum(line) / len(line) for line in warped])
cuts = []
i = 0
while(i < len(color_level)):
if color_level[i] > 250:
begin = i
while(color_level[i] > 250):
i += 1
cuts.append((i + begin)/2) # middle of the whitespace region
i += 1

EDIT 2: Sample image added
enter image description here

Answer Source

From your input image, you need to make text as white, and background as black

enter image description here

You need then to compute the rotation angle of your bill. A simple approach is to find the minAreaRect of all white points (findNonZero), and you get:

enter image description here

Then you can rotate your bill, so that text is horizontal:

enter image description here

Now you can compute horizontal projection (reduce). You can take the average value in each line. Apply a threshold th on the histogram to account for some noise in the image (here I used 0, i.e. no noise). Lines with only background will have a value >0, text lines will have value 0 in the histogram. Then take the average bin coordinate of each continuous sequence of white bins in the histogram. That will be the y coordinate of your lines:

enter image description here

Here the code. It's in C++, but since most of the work is with OpenCV functions, it should be easy convertible to Python. At least, you can use this as a reference:

#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;

int main()
    // Read image
    Mat3b img = imread("path_to_image");

    // Binarize image. Text is white, background is black
    Mat1b bin;
    cvtColor(img, bin, COLOR_BGR2GRAY);
    bin = bin < 200;

    // Find all white pixels
    vector<Point> pts;
    findNonZero(bin, pts);

    // Get rotated rect of white pixels
    RotatedRect box = minAreaRect(pts);
    if (box.size.width > box.size.height)
        swap(box.size.width, box.size.height);
        box.angle += 90.f;

    Point2f vertices[4];

    for (int i = 0; i < 4; ++i)
        line(img, vertices[i], vertices[(i + 1) % 4], Scalar(0, 255, 0));

    // Rotate the image according to the found angle
    Mat1b rotated;
    Mat M = getRotationMatrix2D(, box.angle, 1.0);
    warpAffine(bin, rotated, M, bin.size());

    // Compute horizontal projections
    Mat1f horProj;
    reduce(rotated, horProj, 1, CV_REDUCE_AVG);

    // Remove noise in histogram. White bins identify space lines, black bins identify text lines
    float th = 0;
    Mat1b hist = horProj <= th;

    // Get mean coordinate of white white pixels groups
    vector<int> ycoords;
    int y = 0;
    int count = 0;
    bool isSpace = false;
    for (int i = 0; i < rotated.rows; ++i)
        if (!isSpace)
            if (hist(i))
                isSpace = true;
                count = 1;
                y = i;
            if (!hist(i))
                isSpace = false;
                ycoords.push_back(y / count);
                y += i;

    // Draw line as final result
    Mat3b result;
    cvtColor(rotated, result, COLOR_GRAY2BGR);
    for (int i = 0; i < ycoords.size(); ++i)
        line(result, Point(0, ycoords[i]), Point(result.cols, ycoords[i]), Scalar(0, 255, 0));

    return 0;
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