# The essence of a handwritten digit

August 13, 2012
By

(This article was first published on bayesianbiologist » Rstats, and kindly contributed to R-bloggers)

If you haven’t yet discovered the competitive machine learning site kaggle.com, please do so now. I’ll wait.

Great – so, you checked it out, fell in love and have made it back. I recently downloaded the data for the getting started competition. It consists of 42000 labelled images (28×28) of hand written digits 0-9. The competition is a straight forward supervised learning problem of OCR (Optical Character Recognition). There are two sample R scripts on the site to get you started. They implement the k-nearest neighbours and Random Forest algorithms.

I wanted to get  started by visualizing all of the training data by rendering some sort of an average of each character. Visualizing the data is a great first step to developing a model. Here’s how I did it:

## Read in data
train<-as.matrix(train)

##Color ramp def.
colors<-c('white','black')
cus_col<-colorRampPalette(colors=colors)

## Plot the average image of each digit
par(mfrow=c(4,3),pty='s',mar=c(1,1,1,1),xaxt='n',yaxt='n')
all_img<-array(dim=c(10,28*28))
for(di in 0:9)
{
print(di)
all_img[di+1,]<-apply(train[train[,1]==di,-1],2,sum)
all_img[di+1,]<-all_img[di+1,]/max(all_img[di+1,])*255

z<-array(all_img[di+1,],dim=c(28,28))
z<-z[,28:1] ##right side up
image(1:28,1:28,z,main=di,col=cus_col(256))
}



Which gives you:
Notice the wobbly looking ’1′. You can see that there is some variance in the angle of the slant, with a tenancy toward leaning right. I image that this is due to the bias toward right handed individuals in the sample.

I also wanted to generate a pdf plot of all of the training set, to get myself an idea of what kind of anonymous instances I should expect.

If you are interested, dear reader, here is my code to do just that.

pdf('train_letters.pdf')
par(mfrow=c(4,4),pty='s',mar=c(3,3,3,3),xaxt='n',yaxt='n')
for(i in 1:nrow(train))
{
z<-array(train[i,-1],dim=c(28,28))
z<-z[,28:1] ##right side up
image(1:28,1:28,z,main=train[i,1],col=cus_col(256))
print(i)
}
dev.off()



Which will give you a 2625 page pdf of every character in the training set which you can, um, casually peruse.
As of the time of writing, the current leading submission has a classification accuracy of 99.27%. There is no cash for this competition, but the knowledge gained from taking a stab at it is priceless. So give it a shot!

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