Platypus: A Generalized Specialist Model for Reading Text in Various Forms
<|reference_start|>Platypus: A Generalized Specialist Model for Reading Text in Various Forms: Reading text from images (either natural scenes or documents) has been a long-standing research topic for decades, due to the high technical challenge and wide application range. Previously, individual specialist models are developed to tackle the sub-tasks of text reading (e.g., scene text recognition, handwritten text recognition and mathematical expression recognition). However, such specialist models usually cannot effectively generalize across different sub-tasks. Recently, generalist models (such as GPT-4V), trained on tremendous data in a unified way, have shown enormous potential in reading text in various scenarios, but with the drawbacks of limited accuracy and low efficiency. In this work, we propose Platypus, a generalized specialist model for text reading. Specifically, Platypus combines the best of both worlds: being able to recognize text of various forms with a single unified architecture, while achieving excellent accuracy and high efficiency. To better exploit the advantage of Platypus, we also construct a text reading dataset (called Worms), the images of which are curated from previous datasets and partially re-labeled. Experiments on standard benchmarks demonstrate the effectiveness and superiority of the proposed Platypus model. Model and data will be made publicly available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/Platypus.<|reference_end|>
@article{wang2024platypus:,
    title={Platypus: A Generalized Specialist Model for Reading Text in Various
  Forms},
    author={Peng Wang, Zhaohai Li, Jun Tang, Humen Zhong, Fei Huang, Zhibo Yang,
  Cong Yao},
    journal={arXiv preprint arXiv:2408.14805},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14805},
    primaryClass={cs.CV}
}