Split text lines in scanned document

Question

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     else:         i += 1 

EDIT 2: Sample image added

Answer 1

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

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:

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

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:

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];     box.points(vertices);      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.center, 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;             }         }         else         {             if (!hist(i))             {                 isSpace = false;                 ycoords.push_back(y / count);             }             else             {                 y += i;                 count++;             }         }     }      // 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; }