Advice on using OCR on an image of a blackboard

Question

I'm trying to get an image of a blackboard readable by OCR. Naturally, most OCR software doesn't like dirty images. What image processing should I try to put the image through to clean the image up?

Answer 1

Offhand, I'd say invert the image (reverse the colors, so that the writing is black on white) and increase the contrast a bit. You can try modifying the brightness to get the erased chalk fogginess to disappear into the background.

Answer 2

In Photoshop, the Levels dialog may be your most useful image adjustment. Mimicking this in code is another subject, entirely.

The basis of Levels is that you adjust the max, min and midpoints of the brightness levels. Usually shown on a histogram, you adjust the points such that you obtain the desired amount of contrast, but also move the midpoint such that text in the image is the most well-defined; critical for OCR applications. By moving the midpoint you can "eliminate" the grayscale fuzz that ordinarily surrounds handwriting by causing it to disappear into the light (or dark) areas of the image.

Also you might try converting the image to 1-bit after such an adjustment, forcing everything to black or white. Sometimes this speeds up the OCR process. But be careful, it also will discard detail.

Answer 3

Have you tried edge detection techniques such as Roberts Cross and Sobel operator to filter noise out of the image? Without seeing the quality of the image, can't say how effective that'd be.

Answer 4

There are commercial solutions but cleaning up board images appears to be an open problem. Add OCR to an unsolved problem, and you get... an unsolved problem.

Answer 5

Not sure how constrained you are in the choice of OCR solution, but the ABBYY OCR engine (and a web API based on it, http://www.wisetrend.com/wisetrend_ocr_cloud.shtml ) includes automatic image cleanup / texture removal options.

Answer 6

Have you tried the OCR software yet? It's likely that the OCR software is well suited to reading what's essentially already a black and white image.

However, if you were required to do so you could try to:

1. Threshold the image.
   • Essentially take a greyscale version of the image and turn it into black / white pixels
2. Perform Binary Dilation to grow the remaining objects
3. Perform Binary Erosion

The idea is by dilating then eroding you would remove any rough / noisy edges and then you can pass the skeletonized image to the OCR.

There are probably plenty of methods to achieve a similar result. Given that there are entire books devoted to computer vision this answer will hardly do them justice.

The only texts I have are from 1997, but surely there's been more written on the subject since.

• Algorithms for Image Processing and Computer Vision - J.R. Parker
• Digital Image Processing - Gonzalez / Woods