SEE: Towards Semi-Supervised End-to-End Scene Text Recognition


  • Christian Bartz Hasso Plattner Institute
  • Haojin Yang Hasso Plattner Institute
  • Christoph Meinel Hasso Plattner Institute



Computer Vision, Semi-Supervised Learning


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.




How to Cite

Bartz, C., Yang, H., & Meinel, C. (2018). SEE: Towards Semi-Supervised End-to-End Scene Text Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).