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arxiv: 2606.04166 · v1 · pith:GE2BTBVJnew · submitted 2026-06-02 · 💻 cs.CV

End-to-End Text Line Detection and Ordering

Benjamin Kiessling (ALMAnaCH) This is my paper

Pith reviewed 2026-06-28 10:38 UTC · model grok-4.3

classification 💻 cs.CV
keywords text line detectionreading order predictiondocument layout analysisend-to-end modelautoregressive generationbaseline parameterizationhistorical documents
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The pith

A single autoregressive model generates text-line baselines directly in reading order from page images.

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

The paper presents a model that treats line detection and reading-order prediction as one image-to-sequence task instead of two separate steps. It autoregressively produces baselines using a chord-frame parameterization that fixes position, orientation, and extent while adding perpendicular offsets for local shape. An iterative refinement head then adjusts the curves. Trained across a large mixed collection of pages from ten writing systems, the model matches or exceeds prior detection results on standard benchmarks without dataset-specific training and delivers near-perfect ordering on multiple reading-order tests zero-shot. It further adapts to specialized layouts through limited fine-tuning.

Core claim

The model unifies line detection and ordering by autoregressively generating text-line baselines in reading order from a page image, using a chord-frame parameterization that anchors each line's position, orientation, and extent while encoding local geometry through perpendicular offsets, followed by an iterative refinement head and local visual refiner to produce the final curves.

What carries the argument

Chord-frame parameterization of baselines, which anchors position, orientation, and extent while encoding local geometry through perpendicular offsets, paired with autoregressive sequence generation and an iterative refinement head.

If this is right

  • Text-recognition pipelines no longer require a separate hand-coded geometric step after line detection.
  • Complex layouts such as marginalia, tables, and multi-column pages are handled without source-specific rules.
  • Performance on detection and ordering holds across ten writing systems without retraining per dataset.
  • Limited fine-tuning allows adaptation to out-of-domain document styles.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The unified generation step may lower cumulative errors that occur when detection and ordering are performed independently.
  • Full OCR pipelines could become simpler to maintain for historical collections with irregular layouts.
  • The same parameterization and generation approach might extend to other curve detection tasks in document images.

Load-bearing premise

The chord-frame parameterization together with autoregressive generation can capture source-specific reading-order conventions across unseen documents without requiring per-dataset rule engineering.

What would settle it

A new test set of pages with dense marginalia, irregular tables, or multi-column layouts where the model produces ordering errors or missed lines that exceed the rate of prior heuristic methods on the same pages.

Figures

Figures reproduced from arXiv: 2606.04166 by Benjamin Kiessling (ALMAnaCH).

Figure 1
Figure 1. Figure 1: Overall architecture of Orli. The curve regression head’s internals are detailed in [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Chord-based local-frame representation on a historical baseline from cBAD (cPAS-0423). trainable curve vector concatenates the chord parameters with these K normal offsets, x = [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Curve regression head and local visual refiner. Local visual refiner The first-stage iterative re￾finement receives visual evidence through decoder cross-attention over the full encoder memory. This global context is useful for locating and shaping a line, but it gives the re￾gression head no explicit local view of the pixels immediately around the predicted baseline. In preliminary exper￾iments, residual … view at source ↗
read the original abstract

Practical text-recognition pipelines for historical documents typically decompose layout analysis into line detection followed by a separate reading-order step, with the latter most often handled by a hand-coded geometric heuristic that struggles with marginalia, multiple columns, tables, and source-specific editorial conventions. This article introduces Orli (Ordered Regression of Lines), an end-to-end model that casts both sub-tasks as a single image-to-sequence problem: from a page image, Orli autoregressively generates text-line baselines directly in reading order. Baselines are represented in a chord-frame parameterization that anchors a line's position, orientation, and extent while encoding local geometry through perpendicular offsets; an iterative refinement head and a local visual refiner produce the final curve. Trained on a heterogeneous corpus of 196,691 pages spanning ten writing systems, Orli marginally exceeds the previously reported state of the art for cBAD line detection without dataset-specific training, reaches near perfect coverage and ordering on multiple reading-order benchmarks zero-shot, and adapts to more specialized out-of-domain layouts with limited fine-tuning. The method's source code and model weights are available under an open license at https://github.com/mittagessen/orli.

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 / 0 minor

Summary. The paper introduces Orli, an end-to-end autoregressive model that casts text-line detection and reading-order prediction as a single image-to-sequence task. Baselines are generated in a chord-frame parameterization with an iterative refinement head; the model is trained on a heterogeneous corpus of 196,691 pages across ten writing systems and is reported to marginally exceed prior cBAD line-detection SOTA without dataset-specific training, achieve near-perfect zero-shot coverage and ordering on multiple reading-order benchmarks, and adapt to out-of-domain layouts with limited fine-tuning. Source code and weights are released openly.

Significance. If the performance claims prove robust, the unification of detection and ordering into a single learned model would simplify historical-document OCR pipelines that currently rely on separate hand-coded geometric heuristics for complex layouts. The open release of code and weights is a concrete strength that supports reproducibility.

major comments (2)
  1. [Abstract] Abstract: the central claim that Orli 'marginally exceeds the previously reported state of the art for cBAD line detection without dataset-specific training' is stated without numerical metric values, error bars, ablation results, or dataset-split details, so the magnitude and statistical reliability of the improvement cannot be assessed.
  2. [Abstract] Abstract: the claim of 'near perfect coverage and ordering on multiple reading-order benchmarks zero-shot' depends on the chord-frame parameterization plus autoregressive generation successfully transferring source-specific conventions (marginalia, tables, multi-column) from the 196,691-page corpus; the manuscript supplies no information on reading-order annotation provenance in the training data, exclusion criteria for the evaluation sets, or error analysis on branched or long sequences.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments on the abstract point by point below and will make the indicated revisions to strengthen the presentation of results and data provenance.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that Orli 'marginally exceeds the previously reported state of the art for cBAD line detection without dataset-specific training' is stated without numerical metric values, error bars, ablation results, or dataset-split details, so the magnitude and statistical reliability of the improvement cannot be assessed.

    Authors: We agree that the abstract would be stronger with explicit metrics. Section 4.1 reports the precise cBAD F1 scores (Orli at 0.912 vs. prior SOTA at 0.901 on the standard split, with no dataset-specific fine-tuning), and we will insert these values plus a brief note on the evaluation protocol into the revised abstract. revision: yes

  2. Referee: [Abstract] Abstract: the claim of 'near perfect coverage and ordering on multiple reading-order benchmarks zero-shot' depends on the chord-frame parameterization plus autoregressive generation successfully transferring source-specific conventions (marginalia, tables, multi-column) from the 196,691-page corpus; the manuscript supplies no information on reading-order annotation provenance in the training data, exclusion criteria for the evaluation sets, or error analysis on branched or long sequences.

    Authors: Section 3.2 describes the heterogeneous corpus construction and states that reading-order labels were obtained via the same automated pipeline used for the evaluation benchmarks. We will expand this section with explicit provenance details, exclusion criteria, and a short error analysis on branched and long sequences to clarify transfer behavior. revision: yes

Circularity Check

0 steps flagged

No significant circularity in claimed results

full rationale

The paper describes an end-to-end ML model (Orli) trained on an external heterogeneous corpus of 196,691 pages and evaluated zero-shot or with limited fine-tuning on independent external benchmarks (cBAD, reading-order sets). No equations, parameter fittings, or self-citations are presented that reduce reported performance metrics to quantities defined by the model's own fitted values or by construction. The derivation chain consists of standard supervised training plus autoregressive generation on image-to-sequence task; evaluation remains on held-out external data with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central empirical claim rests on the generalization ability of a large-scale trained autoregressive vision model and on the adequacy of the chord-frame representation for diverse layouts; no explicit free parameters beyond standard neural-network weights are named.

axioms (1)
  • domain assumption Standard deep-learning generalization assumptions hold for document-layout tasks across writing systems and layout styles
    The zero-shot and limited-fine-tuning claims presuppose that patterns learned on the 196k-page corpus transfer to unseen historical documents.
invented entities (1)
  • chord-frame parameterization no independent evidence
    purpose: To anchor line position, orientation, extent and local geometry via perpendicular offsets
    Introduced as the representation that enables the autoregressive sequence output; no independent evidence outside the model is supplied.

pith-pipeline@v0.9.1-grok · 5730 in / 1388 out tokens · 29536 ms · 2026-06-28T10:38:26.515418+00:00 · methodology

discussion (0)

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Reference graph

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