Researchers have developed a novel training-free framework for inferring reading order in complex document layouts, particularly beneficial for digitizing historical manuscripts. This graph-based approach treats OCR text lines as nodes, scoring transitions using language model signals like conditional likelihood and BERT's next-sentence prediction. To mitigate cascading errors, it employs a max-regret inference rule, prioritizing high-opportunity-cost commitments. The method significantly outperforms existing techniques like XY-cut and LayoutReader on challenging layouts, achieving 95% successor edge accuracy on Glossa Ordinaria and 88% on a multi-column subset of OmniDocBench. AI
IMPACT Improves document digitization accuracy, particularly for historical texts with complex layouts.
RANK_REASON The item is an academic paper detailing a new method for document layout analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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