Fixing the cropping issue

I've spent some time now fixing up my cropping function. The reason I've been spending my time on this is because I think that it's really important to have quality training data. If my training data is crap, then nothing will work. I want to be able to cleanly cut out letters. So, I changed my cropping algorithm to the following:
cut the original image into 4 pieces, as follows:


Here is the left piece:


Right piece:


Top piece:


Bottom piece:


I run the hough transform on all four pieces. In each, I know where a line would be, if there exists one. I have some way of thresholding whether or not there is a line there (if the maximum number of votes is higher than a certain percentage of the length of the image). If so, I cut the appropriate piece out.

The performance isn't as great as expected. I think that I need to fix up my hough transform function because sometimes, when there appears to be a dark line, it won't find it (the number of votes is low).

Here are the overall results anyway:


The letters straight out of the training sheet look like this, with no postprocessing:


The next step for me is to fix my hough transform function, get the training data looking good, retrying nearest neighbor, and then moving on to opencv (boosting).

Cropping

I get better accuracy now, when I try to classify my test data. However, I noticed that after my "cropping" tool, the test data can get pretty deformed. For example, take a look at a before and after sequence:

Before:


After:


I decreased the dimensionality of the test data by a lot. The new test data characters are 32 by 32 pixels. Before this, it was 120 by 123 pixels. This reduction in dimensionality really brought down the running time of classification. I think that I need to tweak my cropping tool though.

Cropping tool = bad. Exhibit A:
=

When I change it that only 2% of the pixels can be all white (instead of 5%), then the cut out changes to:


Now, the same test set looks like this:

Fixing up training data

I noticed that the actual letters in my training data take up a small area in the actual image. This seems bad, so I fixed up my code to minimize the amount of white space surrounding the actual letter.

Example of old training data:



Example of new training data:

I'm baaaack

Didn't get enough last year? Miss me? Well, I'm back for more. I will be continuing the same project. Here is a refresher of how the project works:



The first thing that I will be doing this week is getting back to the point I was at at the end of winter 2008. I will then research machine learning methods to apply to the problem, to replace the nearest neighbor approach.