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Abstract:
A recent approach for offline handwriting recognition is to use multidimensional recurrent neural
networks (MDRNN) with connectionist temporal classification which has shown to yield very good
results on several datasets. MDRNNs contain special units – multidimensional Long Short-Term
Memory (MDLSTM) cells. These cells suffer from instability especially for higher dimensionality.
We analyze the reasons for this effect and introduce several cells with better stability. We present
a method to design stable multidimensional cells using the theory of linear shift invariant systems.
The new cells are compared to MDLSTMs on the Arabic and French ICDAR datasets, where they
yield better results.