title: General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
summary: Traditional OCR systems (OCR-1.0) are increasingly unable to meet people's
  usage due to the growing demand for intelligent processing of man-made optical
  characters. In this paper, we collectively refer to all artificial optical
  signals (e.g., plain texts, math/molecular formulas, tables, charts, sheet
  music, and even geometric shapes) as "characters" and propose the General OCR
  Theory along with an excellent model, namely GOT, to promote the arrival of
  OCR-2.0. The GOT, with 580M parameters, is a unified, elegant, and end-to-end
  model, consisting of a high-compression encoder and a long-contexts decoder. As
  an OCR-2.0 model, GOT can handle all the above "characters" under various OCR
  tasks. On the input side, the model supports commonly used scene- and
  document-style images in slice and whole-page styles. On the output side, GOT
  can generate plain or formatted results (markdown/tikz/smiles/kern) via an easy
  prompt. Besides, the model enjoys interactive OCR features, i.e., region-level
  recognition guided by coordinates or colors. Furthermore, we also adapt dynamic
  resolution and multi-page OCR technologies to GOT for better practicality. In
  experiments, we provide sufficient results to prove the superiority of our
  model.
paper_authors._id: 66d8f9357765323b81bc6c53
paper_authors.name: Haoran Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-13T12:10:14.107Z
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paper_authors.fullname: Haoran Wei
paper_authors.type: user
paper_authors.user: HaoranWei
paper_authors._id: 66d8f9357765323b81bc6c54
paper_authors.name: Chenglong Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-13T12:10:23.238Z
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paper_authors.fullname: liu
paper_authors.type: user
paper_authors.user: chenglongliu
paper_authors._id: 66d8f9357765323b81bc6c55
paper_authors.name: Jinyue Chen
paper_authors._id: 66d8f9357765323b81bc6c56
paper_authors.name: Jia Wang
paper_authors._id: 66d8f9357765323b81bc6c57
paper_authors.name: Lingyu Kong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-27T06:59:58.205Z
paper_authors._id: 650d23b69169ea733178f508
paper_authors.avatarUrl: /avatars/5f9a7ab6b6a44e74c3eb6a59da2e21ed.svg
paper_authors.fullname: Lingyu Kong
paper_authors.type: user
paper_authors.user: kppkkp
paper_authors._id: 66d8f9357765323b81bc6c58
paper_authors.name: Yanming Xu
paper_authors._id: 66d8f9357765323b81bc6c59
paper_authors.name: Zheng Ge
paper_authors._id: 66d8f9357765323b81bc6c5a
paper_authors.name: Liang Zhao
paper_authors._id: 66d8f9357765323b81bc6c5b
paper_authors.name: Jianjian Sun
paper_authors._id: 66d8f9357765323b81bc6c5c
paper_authors.name: Yuang Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-13T12:11:40.254Z
paper_authors._id: 631ee086c1a8269da39265c6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/631ee086c1a8269da39265c6/wUa1epGtTGcUv2mvrLUcD.png
paper_authors.fullname: Yuang Peng
paper_authors.type: user
paper_authors.user: yuangpeng
paper_authors._id: 66d8f9357765323b81bc6c5d
paper_authors.name: Chunrui Han
paper_authors._id: 66d8f9357765323b81bc6c5e
paper_authors.name: Xiangyu Zhang
paper_title: General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
paper_summary: Traditional OCR systems (OCR-1.0) are increasingly unable to meet people's
  usage due to the growing demand for intelligent processing of man-made optical
  characters. In this paper, we collectively refer to all artificial optical
  signals (e.g., plain texts, math/molecular formulas, tables, charts, sheet
  music, and even geometric shapes) as "characters" and propose the General OCR
  Theory along with an excellent model, namely GOT, to promote the arrival of
  OCR-2.0. The GOT, with 580M parameters, is a unified, elegant, and end-to-end
  model, consisting of a high-compression encoder and a long-contexts decoder. As
  an OCR-2.0 model, GOT can handle all the above "characters" under various OCR
  tasks. On the input side, the model supports commonly used scene- and
  document-style images in slice and whole-page styles. On the output side, GOT
  can generate plain or formatted results (markdown/tikz/smiles/kern) via an easy
  prompt. Besides, the model enjoys interactive OCR features, i.e., region-level
  recognition guided by coordinates or colors. Furthermore, we also adapt dynamic
  resolution and multi-page OCR technologies to GOT for better practicality. In
  experiments, we provide sufficient results to prove the superiority of our
  model.
paper_ai_summary: GOT, a unified OCR model with 580M parameters, handles various types of characters and OCR tasks and offers advanced features like region-level recognition and dynamic resolution.
paper_ai_keywords: high-compression encoder long-contexts decoder OCR-2.0 region-level recognition dynamic resolution multi-page OCR