SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2017
- DOI:
- 10.48550/arXiv.1712.05404
- arXiv:
- arXiv:1712.05404
- Bibcode:
- 2017arXiv171205404B
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- AAAI-18. arXiv admin note: substantial text overlap with arXiv:1707.08831