cs.CVAug 25, 2026

RefLAM: A Reference-Grounded Line Annotation Pipeline for Historical Arabic Manuscripts

Authors: Mohamed Guechaoui, Mohamed Diaa Zellagui, Souleyman Chaib, Sahraoui Dhelim

Organizations: Higher School of Computer Science (ESI-SBA), Sidi Bel Abbes, Algeria

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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][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.

Figures & tables

Figure 1: The RefLAM pipeline.
Figure 2: AraMS-28k manuscript samples.
PropertyValue
Books14
Total pages3,043
Fully page-validated548
Line-validated2,495
Main-text lines27,971
Margin lines629
Table 1: AraMS-28k at a glance. Full documentation in [ 2 ] .
BookTypeScriptPagesLinesC=100
book_03PVMaghrebi1723,6618.4%
book_05PVRuq‘ah952,02828.0%
book_06PVNaskh4187499.9%
book_09PVNaskh461,0579.8%
book_10PVLithograph1367414.1%
book_11PVNaskh3061212.1%
Table 2: Per-book alignment agreement. “C=100” is the share of reviewed main lines achieving a perfect alignment score against the reference ( proposition 1 ), plotted in Figure 3 . For LV books, C=100 is 100% by construction : only confidence-100 lines were retained for release from these books ( section 6 ). book_10 is a lithographed printed edition rather than a hand-copied manuscript. Full per-book dataset statistics appear in [ 2 ] .
Figure 3: Confidence-score distribution per book (page-validated books).
Figure 4: Throughput comparison (lines per person-hour).
Model / Test subsetCER (%)
Kraken [ 22 ]
book_03 (Maghrebi)32.71
book_05 (Ruq‘ah)11.65
book_09 (Naskh)22.62
Overall23.31
HATFormer [ 10 ]
Table 3: CER after finetuning Muharaf-pretrained models on AraMS-28k.
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