TY - JOUR AU - Bartz, Christian AU - Yang, Haojin AU - Meinel, Christoph PY - 2018/04/27 Y2 - 2024/03/28 TI - SEE: Towards Semi-Supervised End-to-End Scene Text Recognition JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v32i1.12242 UR - https://ojs.aaai.org/index.php/AAAI/article/view/12242 SP - AB - <p> 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. </p> ER -