Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.18616 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909269568258048 |
|---|---|
| author | Liu, Chang Corbillé, Simon Smith, Elisa H Barney |
| author_facet | Liu, Chang Corbillé, Simon Smith, Elisa H Barney |
| contents | Open-set text recognition, which aims to address both novel characters and previously seen ones, is one of the rising subtopics in the text recognition field. However, the current open-set text recognition solutions only focuses on horizontal text, which fail to model the real-life challenges posed by the variety of writing directions in real-world scene text. Multi-orientation text recognition, in general, faces challenges from the diverse image aspect ratios, significant imbalance in data amount, and domain gaps between orientations. In this work, we first propose a Multi-Oriented Open-Set Text Recognition task (MOOSTR) to model the challenges of both novel characters and writing direction variety. We then propose a Multi-Orientation Sharing Experts (MOoSE) framework as a strong baseline solution. MOoSE uses a mixture-of-experts scheme to alleviate the domain gaps between orientations, while exploiting common structural knowledge among experts to alleviate the data scarcity that some experts face. The proposed MOoSE framework is validated by ablative experiments, and also tested for feasibility on the existing open-set benchmark. Code, models, and documents are available at: https://github.com/lancercat/Moose/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18616 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MOoSE: Multi-Orientation Sharing Experts for Open-set Scene Text Recognition Liu, Chang Corbillé, Simon Smith, Elisa H Barney Computer Vision and Pattern Recognition Open-set text recognition, which aims to address both novel characters and previously seen ones, is one of the rising subtopics in the text recognition field. However, the current open-set text recognition solutions only focuses on horizontal text, which fail to model the real-life challenges posed by the variety of writing directions in real-world scene text. Multi-orientation text recognition, in general, faces challenges from the diverse image aspect ratios, significant imbalance in data amount, and domain gaps between orientations. In this work, we first propose a Multi-Oriented Open-Set Text Recognition task (MOOSTR) to model the challenges of both novel characters and writing direction variety. We then propose a Multi-Orientation Sharing Experts (MOoSE) framework as a strong baseline solution. MOoSE uses a mixture-of-experts scheme to alleviate the domain gaps between orientations, while exploiting common structural knowledge among experts to alleviate the data scarcity that some experts face. The proposed MOoSE framework is validated by ablative experiments, and also tested for feasibility on the existing open-set benchmark. Code, models, and documents are available at: https://github.com/lancercat/Moose/ |
| title | MOoSE: Multi-Orientation Sharing Experts for Open-set Scene Text Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.18616 |