abstract
multidimensional recurrent neural networks (mdrnns) have shown a remarkable performance in the area of speech and handwriting recognition. the performance of an mdrnn is improved by further increasing its depth, and the difficulty of learning the deeper network is overcome by using hessian-free (hf)
optimization. given that connectionist temporal classification (ctc) is utilized as
an objective of learning an mdrnn for sequence labeling, the non-convexity of
ctc poses a problem when applying hf to the network. as a solution, a convex
approximation of ctc is formulated and its relationship with the em algorithm
and the fisher information matrix is discussed. an mdrnn up to a depth of 15
layers is successfully trained using hf, resulting in an improved performance for
sequence labeling.