  Detecting and recognizing text in natural scene images is a challenging, yet
not completely solved task. In recent years several new systems that try to
solve at least one of the two sub-tasks (text detection and text recognition)
have been proposed. In this paper we present SEE, a step towards
semi-supervised neural networks for scene text detection and recognition, that
can be optimized end-to-end. Most existing works consist of multiple deep
neural networks and several pre-processing steps. In contrast to this, we
propose to use a single deep neural network, that learns to detect and
recognize text from natural images, in a semi-supervised way. SEE is a network
that integrates and jointly learns a spatial transformer network, which can
learn to detect text regions in an image, and a text recognition network that
takes the identified text regions and recognizes their textual content. We
introduce the idea behind our novel approach and show its feasibility, by
performing a range of experiments on standard benchmark datasets, where we
achieve competitive results.
