Orli is an autoregressive image-to-sequence model that jointly detects text lines and determines their reading order on historical documents via chord-frame baselines, trained on 196k pages across ten scripts.
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Training-free graph method with LM edge scoring and max-regret path cover recovers 95% successor edges on Glossa wrap-around layouts vs 50% for XY-cut and 88% on OmniDocBench multi-column vs 75% XY-cut.
A parser-oriented refinement stage performs set-level reasoning on detector hypotheses to jointly decide instance retention, refine boxes, and set parser input order, cutting reading order errors to 0.024 on OmniDocBench.
citing papers explorer
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End-to-End Text Line Detection and Ordering
Orli is an autoregressive image-to-sequence model that jointly detects text lines and determines their reading order on historical documents via chord-frame baselines, trained on 196k pages across ten scripts.
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Reading Order Inference for Complex Document Layouts
Training-free graph method with LM edge scoring and max-regret path cover recovers 95% successor edges on Glossa wrap-around layouts vs 50% for XY-cut and 88% on OmniDocBench multi-column vs 75% XY-cut.
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Parser-Oriented Structural Refinement for a Stable Layout Interface in Document Parsing
A parser-oriented refinement stage performs set-level reasoning on detector hypotheses to jointly decide instance retention, refine boxes, and set parser input order, cutting reading order errors to 0.024 on OmniDocBench.