  In this work we present a state-of-the-art approach for unconstrained natural
scene text recognition. We propose a cascade approach that incorporates a
convolutional neural network (CNN) architecture followed by a long short term
memory model (LSTM). The CNN learns visual features for the characters and uses
them with a softmax layer to detect sequence of characters. While the CNN gives
very good recognition results, it does not model relation between characters,
hence gives rise to false positive and false negative cases (confusing
characters due to visual similarities like "g" and "9", or confusing background
patches with characters; either removing existing characters or adding
non-existing ones) To alleviate these problems we leverage recent developments
in LSTM architectures to encode contextual information. We show that the LSTM
can dramatically reduce such errors and achieve state-of-the-art accuracy in
the task of unconstrained natural scene text recognition. Moreover we manually
remove all occurrences of the words that exist in the test set from our
training set to test whether our approach will generalize to unseen data. We
use the ICDAR 13 test set for evaluation and compare the results with the state
of the art approaches [11, 18]. We finally present an application of the work
in the domain of for traffic monitoring.
