  We propose an end-to-end recurrent encoder-decoder based sequence learning
approach for printed text Optical Character Recognition (OCR). In contrast to
present day existing state-of-art OCR solution which uses connectionist
temporal classification (CTC) output layer, our approach makes minimalistic
assumptions on the structure and length of the sequence. We use a two step
encoder-decoder approach -- (a) A recurrent encoder reads a variable length
printed text word image and encodes it to a fixed dimensional embedding. (b)
This fixed dimensional embedding is subsequently comprehended by decoder
structure which converts it into a variable length text output. Our
architecture gives competitive performance relative to connectionist temporal
classification (CTC) output layer while being executed in more natural
settings. The learnt deep word image embedding from encoder can be used for
printed text based retrieval systems. The expressive fixed dimensional
embedding for any variable length input expedites the task of retrieval and
makes it more efficient which is not possible with other recurrent neural
network architectures. We empirically investigate the expressiveness and the
learnability of long short term memory (LSTMs) in the sequence to sequence
learning regime by training our network for prediction tasks in segmentation
free printed text OCR. The utility of the proposed architecture for printed
text is demonstrated by quantitative and qualitative evaluation of two tasks --
word prediction and retrieval.
