  Contrary to popular belief, Optical Character Recognition (OCR) remains a
challenging problem when text occurs in unconstrained environments, like
natural scenes, due to geometrical distortions, complex backgrounds, and
diverse fonts. In this paper, we present a segmentation-free OCR system that
combines deep learning methods, synthetic training data generation, and data
augmentation techniques. We render synthetic training data using large text
corpora and over 2000 fonts. To simulate text occurring in complex natural
scenes, we augment extracted samples with geometric distortions and with a
proposed data augmentation technique - alpha-compositing with background
textures. Our models employ a convolutional neural network encoder to extract
features from text images. Inspired by the recent progress in neural machine
translation and language modeling, we examine the capabilities of both
recurrent and convolutional neural networks in modeling the interactions
between input elements.
