Removing paper folding artifacts - opencv

Question

I need to recognize some handwriting on text written with soft pen. Using OpenCV, different thresholding methods, bilateral filtering etc, I get quite good results extracting text from paper. But I also get artifacts from folding:
I cannot change way how paper is handled or photographed before it will be processed. After thresholding same paper looks like this:
I want to remove these artifacts. Biggest trouble for me is situation when some character like "T" happens to be on this line. Horizontal part of "T" may nicely fit to this line.
What I do now: I can detect if there is a standalone line. If something is few pixels tall and very wide, I eliminate it.
I have been reading a lot of information about shadow elimination (because I assume problem is shadow). But they all expect to work in other context - surveillance video feed or image with color background.
Any ideas?
UPDATE:
Was working on ideas based on similar works: http://ivrgwww.epfl.ch/alumni/fredemba/papers/FFICPR06.pdf
Test input
Output of test code:
Source code:
#include "opencv2/opencv.hpp"
using namespace std;
using namespace cv;
int filt1_trackbar=13;
int filt2_trackbar=49;
int filt3_trackbar=6;
int main( int argc, char** argv ) {
Mat src, shadow;
src = imread( argv[1], 1 );
if( !src.data ) {
return -1;
}
Mat histImage1( src.rows, src.cols, CV_8UC3, Scalar(127,127,127) );
Mat histImage2( src.rows, src.cols, CV_8UC3, Scalar(127,127,127) );
int cn = src.channels();
uint8_t* pixelPtr = (uint8_t*)src.data;
for(int i=0 ; i< src.rows;i++) {
for(int j=0 ; j< src.cols;j++) {
Scalar_<uint8_t> bgrPixel;
bgrPixel.val[0] = pixelPtr[i*src.cols*cn + j*cn + 0]; // B
bgrPixel.val[1] = pixelPtr[i*src.cols*cn + j*cn + 1]; // G
bgrPixel.val[2] = pixelPtr[i*src.cols*cn + j*cn + 2]; // R
if(bgrPixel.val[2] !=0 ) { // avoid division by zero
float a= 100.0*(((float)bgrPixel.val[0] / (float)bgrPixel.val[2])); // B/R
float b= 100.0*(((float)bgrPixel.val[1] / (float)bgrPixel.val[2])); // G/R
if(!isinf(a) && !isinf(b)) {
histImage1.at<Vec3b>(i,j)=Vec3b(a,a,a);
histImage2.at<Vec3b>(i,j)=Vec3b(b,b,b);
}
}
}
}
addWeighted(histImage1, 2.0, histImage2, -1.0, 0, shadow);
Mat hsv1,hsv2;
cvtColor(shadow, hsv1, CV_BGR2HSV);
cvtColor(src, hsv2, CV_BGR2HSV);
vector<Mat> channels1;
vector<Mat> channels2;
split(hsv1, channels1);
split(hsv2, channels2);
addWeighted(channels1[2], 0.5, channels2[2], 0.5, 0, channels1[2]);
insertChannel(channels1[2],hsv2,2);
Mat unshadow;
cvtColor(hsv2,unshadow, CV_HSV2BGR);
namedWindow( "src", WINDOW_NORMAL);
namedWindow( "shadow", WINDOW_NORMAL);
namedWindow( "unshadow", WINDOW_NORMAL);
imshow("src", src);
imshow("shadow", shadow);
imshow("unshadow", unshadow);
imwrite("shadow.png", shadow);
imwrite("unshadow.png", unshadow);
waitKey(0);
return 0;
}
It did improve image but not good enough in my opinion. I was impressed it worked at all on such grayscale context. Maybe someone can spot something wrong?

Answer 1

I will write an "Answer" because it is too much for a comment:
Shadow removal is (in my experience) not easy, you might be interested in this Paper: "Fredembach and Finlayson - Simple Shadow Removal"
Another idea i got a while back while working on a similar problem (i haven't tried it myself):
You basically want to identify big (in comparison to the characters) regions on your image and treat them differently. If you would know the shadow regions you could for example make the pages more uniform by brightening up the darker regions. The Question is how you can obtain this large regions.
You could first colour the dark writing in the same clour as the surrounding paper. Afterwards you could use the Bilateral Filter of OpenCV to get large uniform colour patches. You could identify the borders with a contour detection and you'd know where the paper differs in colour (caused by the shadows).
Hopefully this post shines a new light on your problem and gives you some ideas.