2026-06-04原文2 min readUpdated: 2026-06-04

End-to-End Text Line Detection and Ordering

This paper introduces Orli (Ordered Regression of Lines), an end-to-end model that unifies text line detection and reading order prediction as a single image-to-sequence task. Trained on 196,691 pages across ten writing systems, Orli marginally exceeds state-of-the-art on cBAD line detection without dataset-specific training, achieves near-perfect coverage and ordering on multiple reading-order benchmarks zero-shot, and adapts to specialized out-of-domain layouts with limited fine-tuning. Code and weights are open-sourced.

SourcearXiv Computer VisionAuthor: Benjamin Kiessling (ALMAnaCH)

[2606.04166] End-to-End Text Line Detection and Ordering

[Submitted on 2 Jun 2026]

Title:End-to-End Text Line Detection and Ordering

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Abstract:Practical text-recognition pipelines for historical documents typically decompose layout analysis into line detection followed by a separate reading-order step, with the latter most often handled by a hand-coded geometric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 this https URL.

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2606.04166 [cs.CV]

(or arXiv:2606.04166v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.04166

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Benjamin Kiessling [view email] [via CCSD proxy] [v1] Tue, 2 Jun 2026 19:29:32 UTC (278 KB)

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