I am trying to remove background color so as to improve the accuracy of OCR against images. A sample would look like below:
The following shows a possible strategy for processing your image, and OCR it
The last step is doing an OCR. My OCR routine is VERY basic, so I'm sure you may get better results.
The code is Mathematica code.
Not bad at all!
If your image is captured as RGB, just use the green image or quickly convert the bayer pattern which is probably @misha's convert to greyscale solutions probably do.
Hope this helps someone
Using one line code you can get is using OpenCV and python
#Load image as Grayscale
im = cv2.imread('....../Downloads/Gd3oN.jpg',0)
#Use Adaptivethreshold with Gaussian
th = cv2.adaptiveThreshold(im,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
Here's the result
Here's the link for Image Thresholding in OpenCV
You can do this using GIMP (or any other image editing tool).
Blurred image:
Difference image:
Binary:
If you're doing it as a once-off, GIMP is probably good enough. If you expect to do this many times over, you could probably write an imagemagick script or code up your approach using something like Python and OpenCV.
Some problems with the above approach:
In Imagemagick, you can use the -lat function to do that.
convert image.jpg -colorspace gray -negate -lat 50x50+5% -negate result.jpg
convert image.jpg -colorspace HSB -channel 2 -separate +channel \
-white-threshold 35% \
-negate -lat 50x50+5% -negate \
-morphology erode octagon:1 result2.jpg
You can apply blur to the image, so you get almost clear background. Then divide each color component of each pixel of original image by the corresponding component of pixel on the background. And you will get text on white background. Additional postprocessing can help further.
This method works in the case if text is darker then the background (in each color component). Otherwise you can invert colors and apply this method.