RefLAM: A Reference-Grounded Line Annotation Pipeline for Historical Arabic Manuscripts
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2608.25140 (cs)
[Submitted on 25 Aug 2026]
Title:RefLAM: A Reference-Grounded Line Annotation Pipeline for Historical Arabic Manuscripts
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Abstract:Existing approaches to building line-level Arabic handwritten-text-recognition (HTR) training data either rely on fully manual annotation, which does not scale, or on automatic OCR-to-reference alignment methods not yet extended to multi-script, two-zone (main-plus-margin) manuscript layouts with a provable correctness guarantee. We present RefLAM (Reference-grounded Line Annotation for Manuscripts), a pipeline converting manuscript page images and clean transcriptions into validated, line-level ground truth without sacrificing human oversight. RefLAM couples a deep-learning page-segmentation model with a multimodal large language model (MLLM) for structured OCR and a diacritic-agnostic fuzzy alignment engine that grounds each OCR line in a contiguous span of the reference text, with a character-level confidence score in $[0,100]$. A perfect score is provably equivalent to character-for-character identity of the normalised strings (the Confidence-100 rule), verified with no counterexample across the released corpus. A reviewer can thus trust a perfect score, confirming most lines at a glance rather than retyping them, so annotation becomes triaged, with attention concentrated on uncertain alignments. Across 7 fully page-validated books we measured a 75$\times$ throughput gain over manual annotation (3,000 vs. 40 lines/hr); applying the same guarantee to 7 further books, we retained 16,533 confidence-100 main-text lines within one week, excluding sub-100 lines rather than manually correcting them. Using RefLAM, we release AraMS-28k: 14 historical Arabic manuscript books, 3,043 pages, and 27,971 main-text and 629 margin-line annotations with bounding boxes, layout labels, and insertion anchors for 191 margin entries (30.4%). We also finetune Muharaf-pretrained baselines (including HATFormer) on AraMS-28k and report CER results confirming its practical utility for downstream HTR training.
| Comments: | 11 pages, 6 figures, 3 tables, 2 algorithms |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.25140 [cs.CV] |
| (or arXiv:2608.25140v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.25140
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Mohamed Guechaoui [view email][v1] Tue, 25 Aug 2026 20:46:11 UTC (9,363 KB)
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