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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownFind the best open-source OCR models in one place at Papers with Code [P] →
- LinkedLinked via unknownDiffusionGemma: 4x faster text generation →
- LinkedLinked via arxiv author · 85%Iddo Hakim →
“Reading Order Inference for Complex Document Layouts”
- LinkedLinked via arxiv author · 85%Sharva Gogawale →
“Reading Order Inference for Complex Document Layouts”
- LinkedLinked via arxiv author · 85%Omer Ventura →
“Reading Order Inference for Complex Document Layouts”
- LinkedLinked via arxiv author · 85%Gal Grudka →
“Reading Order Inference for Complex Document Layouts”
- LinkedLinked via arxiv author · 85%Daria Vasyutinsky-Shapira →
“Reading Order Inference for Complex Document Layouts”
- LinkedLinked via arxiv author · 85%Berat Kurar-Barakat →
“Reading Order Inference for Complex Document Layouts”