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Deep LSTM is an ideal candidate for text recognition. However textrecognition involves some initial image processing steps like segmentation oflines and words which can induce error to the recognition system. Withoutsegmentation, learning very long range context is difficult and becomescomputationally intractable. Therefore, alternative soft decisions are neededat the pre-processing level. This paper proposes a hybrid text recognizer usinga deep recurrent neural network with multiple layers of abstraction and longrange context along with a language model to verify the performance of the deepneural network. In this paper we construct a multi-hypotheses tree architecturewith candidate segments of line sequences from different segmentationalgorithms at its different branches. The deep neural network is trained onperfectly segmented data and tests each of the candidate segments, generatingunicode sequences. In the verification step, these unicode sequences arevalidated using a sub-string match with the language model and best firstsearch is used to find the best possible combination of alternative hypothesisfrom the tree structure. Thus the verification framework using language modelseliminates wrong segmentation outputs and filters recognition errors.

A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks