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Creating a convolutional LSTM for Chinese handwriting recognition

I’m trying to create a LSTM network for Chinese handwriting recognition. I’ve never trained one before, and I’m quite lost when it comes to training a network of a certain size.
I’ve been able to find open-source projects that have been trained to do this for English, but I haven’t found anything for Chinese.

The closest project to what I’m looking for appears to be the one from chicory.ai, but it appears to be only for recognition of numbers.

How could I put together a network that I can train for Chinese?

A:

What I have done with my Chinese models is:
First train it to recognize numbers by:

10,000 batches of 1000 examples (chicory is probably not trained for this, I guess it uses the MNIST dataset)

Then train it to recognize handwritten Chinese using 80,000 handwritten samples (chicory is probably not trained for this)

Each handwritten Chinese sample has the same number of strokes as a number (4 strokes for one character)
The handwritten Chinese samples are taken from the same author, so they are of the same style, so model trained for number recognition can be used to recognize handwritten Chinese.

See these for reference:

A:

Chinese handwriting is a difficult problem to solve.
Firstly, the Chinese characters are not like the English letters, they are alphabetic characters (Chinese were not a writing language before

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