SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
This addresses the challenge of scene text recognition for computer vision applications, but it is incremental as it builds on existing methods with a unified approach.
The paper tackles the problem of detecting and recognizing text in natural scene images by proposing SEE, a semi-supervised neural network that integrates detection and recognition into a single end-to-end model, achieving competitive results on standard benchmarks.
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.