arXiv:2607.01018v1 Announce Type: new Abstract: 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-
The continuous advancements in AI and computer vision are enabling solutions for increasingly complex data extraction challenges that were previously intractable with traditional OCR methods.
This development addresses a critical bottleneck in digitizing historical and complex documents, unlocking vast archives of information for analysis, research, and cultural preservation.
The ability to accurately infer reading order in documents with intricate layouts means historical texts can be more fully and correctly understood by automated systems, enhancing data accessibility.
- · Digital humanities researchers
- · Libraries and archives
- · AI/ML developers in document processing
- · Cultural preservation organizations
- · Manual data entry services for complex documents
Improved accuracy and efficiency in digitizing and interpreting historical documents with complex layouts.
New avenues for research and analysis of previously inaccessible or difficult-to-process historical textual data.
Potential for new AI applications that leverage structured historical information to identify patterns or connections across vast datasets.
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