1. Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer
- Author
-
Kittenplon, Yair, Lavi, Inbal, Fogel, Sharon, Bar, Yarin, Manmatha, R., and Perona, Pietro
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Computation and Language ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Computation and Language (cs.CL) ,Machine Learning (cs.LG) - Abstract
Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We introduce TextTranSpotter (TTS), a transformer-based approach for text spotting and the first text spotting framework which may be trained with both fully- and weakly-supervised settings. By learning a single latent representation per word detection, and using a novel loss function based on the Hungarian loss, our method alleviates the need for expensive localization annotations. Trained with only text transcription annotations on real data, our weakly-supervised method achieves competitive performance with previous state-of-the-art fully-supervised methods. When trained in a fully-supervised manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks.
- Published
- 2022