Reading Order Inference for Complex Document Layouts
Reading order inference remains a critical bottleneck in the digitization of complex historical manuscripts, where pages contain multiple spatially interleaved reading streams, the canonical example being the Glossa Ordinaria layout, in which a central text is surrounded by commentaries that wrap around it in non-rectangular, non-convex regions. We present a training-free, graph-based framework: each OCR text line becomes a node in a directed candidate-transition graph, edges are scored by a weighted additive ensemble of two lightweight language-model signals (causal language model conditional likelihood and BERT next-sentence prediction, NSP; a third sentence-embedding signal was evaluat...
| Authors | Iddo Hakim, Sharva Gogawale, Omer Ventura et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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