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arxiv: 2409.01704 · v1 · pith:VPF5YSOVnew · submitted 2024-09-03 · 💻 cs.CV

General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model

Haoran Wei , Chenglong Liu , Jinyue Chen , Jia Wang , Lingyu Kong , Yanming Xu , Zheng Ge , Liang Zhao
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Jianjian Sun Yuang Peng Chunrui Han Xiangyu Zhang
This is my paper

Pith reviewed 2026-05-17 20:46 UTC · model grok-4.3

classification 💻 cs.CV
keywords general OCRunified end-to-end modelOCR-2.0prompt-based outputdocument recognitionmulti-task visionscene text understandingformula and table parsing
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The pith

A single unified model can recognize texts, formulas, tables, charts and more by treating them all as characters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Traditional OCR systems fall short as demand grows for processing diverse man-made visual content. The authors redefine plain texts, math and molecular formulas, tables, charts, sheet music, and geometric shapes collectively as characters under a General OCR Theory. This leads to the GOT model, a 580-million-parameter end-to-end system with a high-compression encoder and long-context decoder. The model accepts scene or document images in slices or full pages and produces plain or formatted outputs such as markdown, tikz, or smiles based on simple prompts while supporting coordinate- or color-guided interactive recognition. If the approach holds, it could consolidate many separate OCR tools into one versatile system for broader practical use.

Core claim

The General OCR Theory treats all artificial optical signals as characters and introduces the GOT model as a unified end-to-end solution for OCR-2.0. With 580M parameters, the model combines a high-compression encoder and long-contexts decoder to process both scene-style and document-style images in slice or whole-page formats. It generates plain or structured results through prompt control and enables interactive features such as region-level recognition guided by coordinates or colors, while incorporating dynamic resolution and multi-page handling.

What carries the argument

The GOT model with its high-compression encoder and long-contexts decoder that uses input prompts to select output formats and supports interactive region guidance.

Load-bearing premise

That one 580M-parameter end-to-end model using prompt-based formatting can sustain high accuracy across all character types and input styles without interference or the need for separate task-specific parts.

What would settle it

A controlled test showing that accuracy on mathematical formulas falls when the same model is also trained on tables and charts, or that the model requires separate fine-tuning for different character types to reach competitive performance.

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes the General OCR Theory, redefining all artificial optical signals (plain texts, math/molecular formulas, tables, charts, sheet music, geometric shapes) as 'characters'. It introduces the GOT model: a 580M-parameter unified end-to-end system with a high-compression encoder and long-context decoder. GOT supports scene/document images (slice or whole-page), outputs plain or formatted results (markdown/tikz/smiles/kern) via prompts, enables interactive region-level recognition by coordinates or colors, and incorporates dynamic resolution and multi-page processing. The authors state that experiments provide sufficient results proving the model's superiority for advancing OCR-2.0.

Significance. If the empirical claims hold, this unified approach could reduce reliance on task-specific OCR pipelines and enable more flexible handling of diverse document and scene content. The prompt-based output formatting and interactive features are practical strengths. However, significance hinges on demonstrating that a single shared decoder maintains high accuracy across conflicting output formats without negative transfer, which is not yet substantiated by the available details.

major comments (2)
  1. [Architecture] Model description (high-compression encoder + long-context decoder): no inductive bias, regularization, or task routing is described to prevent negative transfer when jointly optimizing cross-entropy over plain text, table markdown, SMILES, and TikZ outputs. This directly undermines the central claim that one 580M-parameter model can handle all listed character types without interference or accuracy degradation on structured tasks.
  2. [Experiments] Experiments section: the abstract asserts 'sufficient results to prove the superiority of our model' yet supplies no per-task metrics, baseline comparisons, error bars, or analysis of interference between output vocabularies. Without these, the superiority claim for the unified end-to-end design cannot be evaluated and is load-bearing for the OCR-2.0 contribution.
minor comments (2)
  1. [Introduction] The introduction of 'General OCR Theory' as a named contribution would benefit from a concise formal statement or set of principles rather than solely descriptive text.
  2. [Model] Notation for output formats (markdown/tikz/smiles/kern) is introduced via prompt but lacks an example of the exact prompt template used for each.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments on our paper proposing the General OCR Theory and the GOT model. We address the major comments point by point below, providing clarifications and indicating revisions where appropriate.

read point-by-point responses

  1. Referee: [Architecture] Model description (high-compression encoder + long-context decoder): no inductive bias, regularization, or task routing is described to prevent negative transfer when jointly optimizing cross-entropy over plain text, table markdown, SMILES, and TikZ outputs. This directly undermines the central claim that one 580M-parameter model can handle all listed character types without interference or accuracy degradation on structured tasks.

    Authors: We agree that the current manuscript does not provide a detailed description of mechanisms to mitigate negative transfer. The model relies on prompt engineering to specify the output format, which guides the decoder accordingly. The training data is curated to balance different tasks. To strengthen the paper, we will revise the architecture section to include more details on the training procedure, any regularization techniques employed, and how the shared decoder handles diverse outputs without significant interference. revision: yes

  2. Referee: [Experiments] Experiments section: the abstract asserts 'sufficient results to prove the superiority of our model' yet supplies no per-task metrics, baseline comparisons, error bars, or analysis of interference between output vocabularies. Without these, the superiority claim for the unified end-to-end design cannot be evaluated and is load-bearing for the OCR-2.0 contribution.

    Authors: The experiments in the manuscript do include results across various OCR tasks demonstrating the model's capabilities. However, we acknowledge that a more granular breakdown with per-task metrics, explicit baseline comparisons, and analysis of potential negative transfer would strengthen the claims. In the revised version, we will expand the experiments section to include these details, including any available error bars from repeated evaluations and discussion on output vocabulary interference. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical model proposal with no derivation chain or self-referential reductions

full rationale

The paper proposes a redefinition of 'characters' to encompass diverse artificial optical signals and introduces the GOT model as an end-to-end architecture for OCR-2.0 tasks. No equations, first-principles derivations, or load-bearing predictions appear in the provided text. Claims rest on architectural description (high-compression encoder + long-context decoder) and empirical results rather than any step that reduces by construction to fitted inputs or prior self-citations. The central premise is a practical unification via prompting and dynamic resolution, which is independently testable against benchmarks and does not invoke uniqueness theorems or ansatzes from the authors' prior work. This is a standard model paper whose validity hinges on external validation, not internal definitional loops.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

Review based on abstract only; no detailed equations or methods available. The 580M parameter count is stated but not decomposed. The new theory name and assumption of unified prompt-based handling constitute the main additions.

free parameters (1)
  • Total model parameters
    580M parameter count given without breakdown into specific fitted values or hyperparameters.
axioms (1)
  • domain assumption A single unified end-to-end architecture with prompt control can effectively process all listed artificial optical signal types at high accuracy.
    Central premise invoked when positioning GOT as OCR-2.0 without task-specific modules.
invented entities (1)
  • General OCR Theory no independent evidence
    purpose: To provide a unifying conceptual framework that treats diverse man-made optical signals as equivalent 'characters'.
    Introduced in the paper to motivate the model; no prior literature reference visible in abstract.

pith-pipeline@v0.9.0 · 5569 in / 1507 out tokens · 45207 ms · 2026-05-17T20:46:27.603037+00:00 · methodology

discussion (0)

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