I was going to be doing this project in Matlab and using a multilayer perceptron as my classifier. However I've since made the decision to work on this in C++/OpenCV and use an SVM.
Reason for C++:
I've been working with Kai Wang, a graduate student in the CSE dept. He is also working with text detection and his project is implemented in C++ with OpenCV. I figured that it would be a lot easier to utilize his help if the two projects were under the same platform.
Reason for SVM:
In my experience, ANNs can be very effective classifiers but can take a very long time to train properly. Since this project will only be active for 6 more weeks, I think that an SVM is a safer bet.
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First feature set I will be trying: Image integral boxes
By taking various per-pixel features, such as brightness, gradient magnitude, and gradient orientation, and then computing the sum/std of various boxes of that information, we can achieve a reasonable feature set for detecting text.
(Image from Chen & Yuille)
Once the features have been computed for all regions of an image, this vector can be sent to the classifier to determine whether or not a given region contains text.
To give everyone an idea of what kind of image I'll be training on, here is an example from the training set:
TODO: for next week.
Implement the full pipeline of load image -> compute feature set -> pass into training algorithm for text detection. I want to get a basic pipeline running and then will debug and add more interesting features for detection.
