AI Neural Network Wrong Handwritten Digit Prediction due to inverted color. Octave/Matlab?

Question

my program in Octave uses Neural Networks to recognize handwritten digits. The problem is that it will not recognize the digit correctly if the color is changed. But if color is inverted, it predicts incorrectly. For example:

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The Images above contain same number with same pattern. But they have inverted colors.

I am already using RGB to GrayScale Conversions. How to overcome this problem? Is there any better option than using separate training examples for inverted colored images?

Answer 1

Edge Extraction

If you extract the edges from your images, you'll see that it is largely invariant in that regard, both versions of your image look almost identical after the transformation

Below I show how the image looks when you extract the edges using Laplacian edge detection, for both the "white on black" and "black on white" images:

The idea is to train your network on the edges, to gain some invariance with regard to the variation you described.

Here are some resources for MATLAB/OCtave for edge extraction:

https://mathworks.com/discovery/edge-detection.html https://octave.sourceforge.io/image/function/edge.html

_{I've done the edge extraction using Python and OpenCV with edges_image = cv2.Laplacian(original_image, cv2.CV_64F) . I may post a MATLAB/Octave sample if I can fix my install :)}

Detect dominant color and invert if required

Another way would be to decide that you want to use a version, let's say you've trained the network on "Black text on white background" variant.

Now when you input the image, first detect if the dominant color / background is black or white, then invert if required.

Answer 2

Feature Extraction

To generalise @bakkal's suggestion of using edges, one can extract many types of image features . These include edges, corners, blobs, ridges, etc.. There is actually a page on mathworks with a few examples, including number recognition using HOG features (histogram of oriented gradients).

Such techniques should work for more complex images too, because edges are not always the best features. Extracting HOG features from the two of your images using matlab's extractHOGFeatures :

I believe you can use vlfeat for HOG features if you have Octave instead.

Another important thing to keep in mind is that you want all images to have the same size. I have resized both of your images to be 500x500, but this is arbitrary.

The code to generate the image above

close all; clear; clc;

% reading in
img1 = rgb2gray(imread('img1.png'));
img2 = rgb2gray(imread('img2.png'));

img_size = [500 500]; % 

% all images should have the same size
img1_resized = imresize(img1, img_size);
img2_resized = imresize(img2, img_size);

% extracting features
[hog1, vis1] = extractHOGFeatures(img1_resized);
[hog2, vis2] = extractHOGFeatures(img2_resized);

% plotting
figure(1);
subplot(1, 2, 1);
plot(vis1);
subplot(1, 2, 2);
plot(vis2);

You do not have to be limited to HOG features. One can also quickly try SURF features

Again, the color inversion does not matter because the features match. But you can see that HOG features are probably a better choice here, because the plotted 20 points/blobs do not really represent number 6 that well.. The code to get the above in matlab.

% extracting SURF features
points1 = detectSURFFeatures(img1_resized);
points2 = detectSURFFeatures(img2_resized);

% plotting SURF Features
figure(2);
subplot(1, 2, 1);
imshow(img1_resized);
hold on;
plot(points1.selectStrongest(20));
hold off;
subplot(1, 2, 2);
imshow(img2_resized);
hold on;
plot(points2.selectStrongest(20));
hold off;

To summarise, depending on the problem, you can choose different types of features. Most of the time choosing raw pixel values is not good enough as you saw from your own experience, unless you have a very large dataset encapsulating all possible cases.