TY  - GEN
A1  - Bartz, Christian
A1  - Yang, Haojin
A1  - Meinel, Christoph
T1  - SEE: Towards semi-supervised end-to-end scene text recognition
T2  - Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, Thirtieth Innovative Applications of Artificial Intelligence Conference, Eight Symposium on Educational Advances in Artificial Intelligence
N2  - 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.
Y1  - 2018
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/53978
SN  - 978-1-57735-800-8
VL  - 10
SP  - 6674
EP  - 6681
PB  - ASSOC Association for the Advancement of Artificial Intelligence
CY  - Palo Alto
ER  -