Hi I have been searching though research papers on what features would be good for me to use in my handwritten OCR classifying neural network. I am a beginner so I have been just taking the image of the handwritten character, made a bounding box around it, and then resize it into a 15×20 binary image. So this means i have an input layer of 300 features. From the papers i have found on google (most of which are quite old) the methods really vary. My accuracy is not bad with just a binary grid of the image, but I was wondering if anyone had other features I could use to boost my accuracy. Or even just pointing me in the right direction. I would really appreciate it!
Thanks,
Zach
I haven’t read any actual papers on this topic, but my advice would be to get creative. Use anything you could think of that might help the classifier identify numbers.
My first thought would be to try and identify “lines” in the image, maybe via a modified “sliding window” algorithm (sliding/rotating line?), or to try and identify a “line of best fit” to the image (to help the classifier respond to changes in italicism or writing style). Really though, if you’re using a neural network, it should be picking up on these sorts of things without your manual help (that’s the whole point of them!)
I would focus first on the structure and topology of your net to try and improve performance, and worry about additional features only if you cannot get satisfactory performance some other way. Also you could try improving the features you already have, make sure the character is centered in the image, maybe try an algorithm to skew italicised characters to make them vertical?
In my experience these sorts of things don’t often help, but you could get lucky and run into one that improves your net 🙂