Models and Algorithms for Reliable Recognition of Scanned Uzbek Cyrillic and Latin Texts by Staged Low-Rank Adaptation of a Locally Deployed Multimodal Model

Large collections of Uzbek books and dissertations exist only as scans, and general-purpose OCR confuses the Uzbek Cyrillic letters ў, қ, ғ and ҳ with their Russian look-alikes. We present uzocr-8b, an open full-page OCR model for Uzbek Cyrillic and Latin obtained by staged LoRA adaptation of Qwen3-VL-8B-Instruct on exactly labelled synthetic pages and pseudo-labelled real scans. We formalise the model and training objective, a repetition-loop criterion and retry policy, and a conservative lexicon rule for the four Uzbek-specific letters. We describe a page-level benchmark of 300 pages (61,370 reference words) from 11 held-out typeset and letterpress Uzbek documents printed between 1999 and 2023, with a reference checked against the scans, and an evaluation protocol with fixed normalisation, corpus-level error rates and bootstrap confidence intervals. On the 284 pages retained by a coverage filter, uzocr-8b reaches a normalised word error rate of 1.42% without post-processing, 1.74% on Cyrillic and 0.65% on Latin pages, against 11.98% for its base model, 7.91% for Qwen3.6-35B-A3B and 25.64% for Tesseract 5; on all 300 pages, 4.03% against 14.65%, 10.16% and 27.53%. The synthetic-only checkpoint reaches 3.17%. One RTX 5090 processes a page in about 1.2–2.0 s. The weights are released under Apache-2.0.