image processing to improve tesseract OCR accuracy
Three points to improve the readability of the image:
Resize the image with variable height and width(multiply 0.5 and 1 and 2 with image height and width).
Convert the image to Gray scale format(Black and white).
Remove the noise pixels and make more clear(Filter the image).
Refer below code :
Resize
public Bitmap Resize(Bitmap bmp, int newWidth, int newHeight)
{
Bitmap temp = (Bitmap)bmp;
Bitmap bmap = new Bitmap(newWidth, newHeight, temp.PixelFormat);
double nWidthFactor = (double)temp.Width / (double)newWidth;
double nHeightFactor = (double)temp.Height / (double)newHeight;
double fx, fy, nx, ny;
int cx, cy, fr_x, fr_y;
Color color1 = new Color();
Color color2 = new Color();
Color color3 = new Color();
Color color4 = new Color();
byte nRed, nGreen, nBlue;
byte bp1, bp2;
for (int x = 0; x < bmap.Width; ++x)
{
for (int y = 0; y < bmap.Height; ++y)
{
fr_x = (int)Math.Floor(x * nWidthFactor);
fr_y = (int)Math.Floor(y * nHeightFactor);
cx = fr_x + 1;
if (cx >= temp.Width) cx = fr_x;
cy = fr_y + 1;
if (cy >= temp.Height) cy = fr_y;
fx = x * nWidthFactor - fr_x;
fy = y * nHeightFactor - fr_y;
nx = 1.0 - fx;
ny = 1.0 - fy;
color1 = temp.GetPixel(fr_x, fr_y);
color2 = temp.GetPixel(cx, fr_y);
color3 = temp.GetPixel(fr_x, cy);
color4 = temp.GetPixel(cx, cy);
// Blue
bp1 = (byte)(nx * color1.B + fx * color2.B);
bp2 = (byte)(nx * color3.B + fx * color4.B);
nBlue = (byte)(ny * (double)(bp1) + fy * (double)(bp2));
// Green
bp1 = (byte)(nx * color1.G + fx * color2.G);
bp2 = (byte)(nx * color3.G + fx * color4.G);
nGreen = (byte)(ny * (double)(bp1) + fy * (double)(bp2));
// Red
bp1 = (byte)(nx * color1.R + fx * color2.R);
bp2 = (byte)(nx * color3.R + fx * color4.R);
nRed = (byte)(ny * (double)(bp1) + fy * (double)(bp2));
bmap.SetPixel(x, y, System.Drawing.Color.FromArgb
(255, nRed, nGreen, nBlue));
}
}
bmap = SetGrayscale(bmap);
bmap = RemoveNoise(bmap);
return bmap;
}
SetGrayscale
public Bitmap SetGrayscale(Bitmap img)
{
Bitmap temp = (Bitmap)img;
Bitmap bmap = (Bitmap)temp.Clone();
Color c;
for (int i = 0; i < bmap.Width; i++)
{
for (int j = 0; j < bmap.Height; j++)
{
c = bmap.GetPixel(i, j);
byte gray = (byte)(.299 * c.R + .587 * c.G + .114 * c.B);
bmap.SetPixel(i, j, Color.FromArgb(gray, gray, gray));
}
}
return (Bitmap)bmap.Clone();
}
RemoveNoise
public Bitmap RemoveNoise(Bitmap bmap)
{
for (var x = 0; x < bmap.Width; x++)
{
for (var y = 0; y < bmap.Height; y++)
{
var pixel = bmap.GetPixel(x, y);
if (pixel.R < 162 && pixel.G < 162 && pixel.B < 162)
bmap.SetPixel(x, y, Color.Black);
else if (pixel.R > 162 && pixel.G > 162 && pixel.B > 162)
bmap.SetPixel(x, y, Color.White);
}
}
return bmap;
}
INPUT IMAGE
OUTPUT IMAGE
- fix DPI (if needed) 300 DPI is minimum
- fix text size (e.g. 12 pt should be ok)
- try to fix text lines (deskew and dewarp text)
- try to fix illumination of image (e.g. no dark part of image)
- binarize and de-noise image
There is no universal command line that would fit to all cases (sometimes you need to blur and sharpen image). But you can give a try to TEXTCLEANER from Fred's ImageMagick Scripts.
If you are not fan of command line, maybe you can try to use opensource scantailor.sourceforge.net or commercial bookrestorer.
As a rule of thumb, I usually apply the following image pre-processing techniques using OpenCV library:
Rescaling the image (it's recommended if you’re working with images that have a DPI of less than 300 dpi):
img = cv2.resize(img, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)Converting image to grayscale:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)Applying dilation and erosion to remove the noise (you may play with the kernel size depending on your data set):
kernel = np.ones((1, 1), np.uint8) img = cv2.dilate(img, kernel, iterations=1) img = cv2.erode(img, kernel, iterations=1)Applying blur, which can be done by using one of the following lines (each of which has its pros and cons, however, median blur and bilateral filter usually perform better than gaussian blur.):
cv2.threshold(cv2.GaussianBlur(img, (5, 5), 0), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] cv2.threshold(cv2.bilateralFilter(img, 5, 75, 75), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] cv2.threshold(cv2.medianBlur(img, 3), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] cv2.adaptiveThreshold(cv2.GaussianBlur(img, (5, 5), 0), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2) cv2.adaptiveThreshold(cv2.bilateralFilter(img, 9, 75, 75), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2) cv2.adaptiveThreshold(cv2.medianBlur(img, 3), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)
I've recently written a pretty simple guide to Tesseract but it should enable you to write your first OCR script and clear up some hurdles that I experienced when things were less clear than I would have liked in the documentation.
In case you'd like to check them out, here I'm sharing the links with you:
Getting started with Tesseract - Part I: Introduction
Getting started with Tesseract - Part II: Image Pre-processing
I am by no means an OCR expert. But I this week had the need to convert text out of a jpg.
I started with a colorized, RGB 445x747 pixel jpg. I immediately tried tesseract on this, and the program converted almost nothing. I then went into GIMP and did the following.
- image > mode > grayscale
- image > scale image > 1191x2000 pixels
- filters > enhance > unsharp mask with values of
radius = 6.8, amount = 2.69, threshold = 0
I then saved as a new jpg at 100% quality.
Tesseract then was able to extract all the text into a .txt file
Gimp is your friend.