letter recognition network

Last week, I wrote a neural network that could balance a stick. That was a simple problem which really only takes a single neuron to figure out.

This week, I promised to write a net which could learn to recognise letters.


For this, I enhanced the network a bit. I added a more sensible weight-correction algorithm, and separated the code (ANN code).

I was considering whether hidden inputs were required at all for this task. I didn’t think so – in my opinion, recognising the letter ‘I’ for example, should depend on some information such as “does it look like a J, and not like an M?” – in other words, recognising a letter depends on how confident you are about whether other values are right or wrong.

The network I chose to implement is, I think, called a “simple recurrent network” with stochastic elements. This means that every neuron reads from every other neuron and not itself, and corrections are not exact – there is a small element of randomness or “noise” in there.

The popular choice for this kind of test is a feed-forward network, which is trained through back-propagation. That network requires hidden units, and each output (is it N, it it Q) is totally ignorant of the other outputs, which I think is a detriment.

My network variant has just finished a training run after 44 training cycles. That is proof that the simple recurrent network can learn to recognise simple letters without relying on hidden units.

Another interesting thing about the method I used is how the training works. Instead of throwing a huge list of tests at the network, I have 26 tests, but only a set number of them are run in each cycle depending on how many were gotten right up until then. For example, a training cycle with 13 tests will only be allowed if the network previously successfully passed 12 tests.

There are still a few small details I’d want to be sure about before pronouncing this test an absolute success, but I’m very happy with it.

Next week, I hope to have this demo re-written in Java, and a new demo recognising flowers in full-colour pictures (stretching myself, maybe…).

As always, this has the end goal of being inserted in a tiny robot which will then do my gardening for me. Not a mad idea, I think you’re beginning to see – just a lot of work.

update As I thought, there were some points which were not quiet perfect. There was a part of the algorithm which would artifically boost the success of the net. With those deficiencies corrected, it takes over 500 cycles to get to 6 correct letters. I think this can be improved… (moments later – now only takes 150+ to reach 6 letters)

2 Replies to “letter recognition network”

  1. persistence pays off – it now takes only about 150 cycles to learn the entire alphabet, and to prove it, a cycle is run after the training which does not do any adjustments at all to the net while running.

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