Reading Order Inference for Complex Document Layouts

Training-free graph-based framework for reading order inference in complex document layouts

A new arXiv preprint presents a training-free, graph-based method for inferring reading order in complex historical document layouts, including the challenging Glossa Ordinaria manuscript format where text and commentary are spatially interleaved. The approach scores edges in a directed candidate-transition graph using lightweight language model signals (causal LM likelihood and BERT NSP) and recovers global reading order via a degree-constrained directed path cover with a max-regret inference rule. On wrap-around Glossa layouts the method achieves 95% edge accuracy versus 50% for XY-cut, and 88% versus 75% for XY-cut and 25% for LayoutReader on OmniDocBench multi-column pages. The work is relevant to document digitization pipelines and OCR post-processing for historical archives.