title: Platypus: A Generalized Specialist Model for Reading Text in Various
    Forms
summary: 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.
paper_authors._id: 66ce83b2da5d6bbe4d002b25
paper_authors.name: Peng Wang
paper_authors._id: 66ce83b2da5d6bbe4d002b26
paper_authors.name: Zhaohai Li
paper_authors._id: 66ce83b2da5d6bbe4d002b27
paper_authors.name: Jun Tang
paper_authors._id: 66ce83b2da5d6bbe4d002b28
paper_authors.name: Humen Zhong
paper_authors._id: 66ce83b2da5d6bbe4d002b29
paper_authors.name: Fei Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-28T09:34:52.501Z
paper_authors._id: 635b8b6a37c6a2c12e2cce00
paper_authors.avatarUrl: /avatars/229fb72180529141515d1df797b33709.svg
paper_authors.fullname: Fei Huang
paper_authors.type: user
paper_authors.user: hzhwcmhf
paper_authors._id: 66ce83b2da5d6bbe4d002b2a
paper_authors.name: Zhibo Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-28T09:34:13.970Z
paper_authors._id: 66051de91b8220d71becb448
paper_authors.avatarUrl: /avatars/f2b507f5821f8baa90b60f62ae4de389.svg
paper_authors.fullname: 杨智博
paper_authors.type: user
paper_authors.user: zhiboyang
paper_authors._id: 66ce83b2da5d6bbe4d002b2b
paper_authors.name: Cong Yao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-28T09:34:03.965Z
paper_authors._id: 6638f898c168eec02354abeb
paper_authors.avatarUrl: /avatars/0a0b7a1b056463fb4ccf6bfe6456a7d1.svg
paper_authors.fullname: Cong Yao
paper_authors.type: user
paper_authors.user: bridgetop3young
paper_title: Platypus: A Generalized Specialist Model for Reading Text in Various
    Forms
paper_summary: 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.
paper_ai_keywords: scene text recognition handwritten text recognition mathematical expression recognition generalist models GPT-4V generalized specialist model dual unified architecture Platypus Worms dataset standard benchmarks