Building and expanding access to digital archives supports language reclamation and creates lasting resources for community revitalization. Designing effective tools to support these efforts requires taking into account the community-, language-, and document-specific context. We investigate how small (≤3B) vision-language models (VLMs) can be adapted as optical character recognition (OCR) tools for Lakota, a North American Indigenous language. In addition to benchmarking modeling approaches, we inspect four practical facets of low-resource archival OCR that arise in previous work and conversations with community members: constrained training budgets, segmentation noise, scan degradation, and orthographic variance. We demonstrate that fine-tuning VLMs for OCR post-correction on 35 pages of transcribed data decreases character error rate (CER) from 17.1 to 3.9% on a bilingual corpus, and find that training only with nine pages increases CER by only 1.1% over this. However, evaluating under realistic automatic segmentation nearly doubles the CER of the best-performing models, stressing the importance of taking realistic use cases into account. We hope this work encourages future research on developing language technologies grounded in the workflows and constraints of ongoing revitalization efforts.
Figures & tables
Figure 1: Overview of some of the challenges in Lakota document transcription (e.g., orthographic variation, document degradation, manual vs. automatic segmentation noise, and constrained training budgets) and where they may arise along an OCR training and inference pipeline.
Sub-corpus
Pages
Lines
Chars
Lakota (%)
Deloria
Lakota
14
299
20,655
96.5%
Mixed
5
51
3,365
39.9%
English
11
136
6,812
30.1%
CULP
Lakota
1
24
142
100.0%
Table 1: Corpus overview across orthographic and language subsets. Lakota(%) is calculated using langdetect.
Model
CER (%)
WER (%)
GLM-OCR (0.9B)
17.09
50.33
Qwen3-VL (2B)
18.06
51.76
Dots.ocr (1.7B)
18.06
46.99
Nanonets OCR2 (3B)
20.32
67.72
Tesseract v5 (lat)
32.58
68.65
Transkribus (Text Titan II)
15.73
47.65
Table 2: Overall zero-shot OCR performance on all transcribed pages.
Figure 2: CER of different OCR approaches across small VLMs with direct finetuning and post-correction.
Figure 3: Solid curves show the average Character Error Rate (CER) across 10 random training-set selections under 10-fold cross-validation (100 trained models per point). Shaded regions show the 95% confidence intervals across the different random training subsets.
Approach
Gold
Kraken
Full Page
GLM-OCRZS
17.09
19.82 (+2.73)
20.06 (+2.97)
TranskribusZS †
15.73
—
18.20 (+2.47)
GLMZS+LSTMPOST
15.46
19.40 (+3.94)
—
GLMZS+LLMPOST
14.06
16.76 (+2.70)
—
GLMZS+ByT5POST
10.59
13.24 (+2.65)
—
GLMSFT
4.39
7.00 (+2.61)
11.97 (+7.58)
Table 3: CER and degradation ( Δ ) of OCR performance relative to gold bounding boxes. SFT and post-correction methods use GLM-OCR. Omitted entries (—) are not applicable to the respective pipeline or were omitted due to commercial constraints.
Orthography
Lakota
Corpus
Deloria
GLMSFT (Deloria Expert)
5.39
6.85
GLMZS+GLMPOST (Deloria Expert)
5.14
6.25
GLMSFT (Unified)
5.44
4.27
GLMZS+GLMPOST (Unified)
5.66
3.81
CULP
Table 4: CER of models trained on orthography-specific subsets versus unified corpus training. The first column indicates the performance on Lakota text only within the specified orthography, and the Corpus column indicates the performance of all text in the orthography.
Configuration
Lak
Overall
High Contrast ( Cw>0.61 )
GLMZS
30.88
28.12
GLMZS+ByT5POST
16.87
15.18
GLMSFT
5.20
4.97
GLMZS+GLMPOST
5.52
5.24
Medium Contrast ( 0.58<Cw≤0.61 )
Table 5: CER (%) across Weber optical contrast tiers on historical Deloria manuscripts.
Table 6: Parallel sentence illustrating orthographic differences between historical Deloria manuscripts and the Suggested Lakota Orthography (SLO).
Figure 4: An example of a Lakota page from the Deloria corpus. This page highlights the handwritten diacritics as well as document degradation.
Figure 5: An example of a bilingual page from the CULP learning materials. This page highlights the clean document quality in comparison to the Deloria materials as well as the more mixed language nature of the text. Where Deloria is easily separable at the page level by language, the Lakota and English are interleaved in the learning materials. Furthermore, the document highlights structural challenges like double columns and hole punches that can confuse automatic segmentation models.
Figure 6: An example of a Lakota sentence, its word-for-word translation, and free translation in the Deloria corpus.
Figure 7: An example of a low and high contrast line from the Deloria documents.
Figure 8: Examples of line crops and the corresponding transcriptions from various OCR and post-correction paradigms with GLM-OCR. We additionally include Tesseract to illustrate higher baseline error rates.
Model
ZS
Own
Best
Ensemble
GLM-OCR(0.9B)
17.09
3.87
3.87
4.05
Qwen3-VL(2.0B)
18.06
4.69
5.20
4.52
Appendix
Table 7: CER across draft conditioning settings. Own refers to post-correcting the zero-shot draft from the same model. Best refers to correcting the zero-shot draft of the VLM with the lowest zero-shot CER (GLM-OCR). Finally, ensemble refers to training the model on each zero-shot draft and corrected version as a separate data point. While we hypothesized that increasing the data size and diversity of errors may improve performance, the ensemble method actually makes performance worse for GLM and slightly improves Qwen3-VL. Because we use GLM-OCR for the majority of experiments, we omit ensembling. This also provides a simpler training setup.
Model
ZS
Diacritic
Base
Transkribus (Text Titan II)
15.73
61.20
5.79
GLM-OCR (0.9B)
17.09
85.28
2.75
Dots.ocr (1.7B)
18.06
74.32
5.28
Qwen3-VL (2B)
18.06
85.06
4.47
Nanonets OCR2 (3B)
20.32
68.37
9.42
Tesseract v5 (Lat)
32.58
84.93
28.14
Appendix
Table 9: Zero-shot character error rates (CER) across OCR and vision-language models, broken down by overall CER, diacritic-only CER, and base character CER (%).
Figure 9: Results of VLMZS+VLMcorr separated by language. The majority of gains is concentrated in Lakota whereas English and mixed language CERs improve little in comparison.
Figure 10: Results of VLMZS+VLMcorr evaluated on gold line-crops versus line-crops produced by Kraken, the automatic segmentation model. Error deltas remain relatively constant across training budgets, indicating that training on gold bounding boxes does not provide any improvements on automatic ones.
Figure 11: Edit operation counts for each of the training-based methods. For Seq2Seq models, deletions make up the largest proportion of edits. However, for LLM and VLM based methods, substitutions are more common, especially at tiny training budgets. For directly finetuning VLMs for OCR, we noticeably see a jump in insertions at 1 page of training data, likely indicating a spike in hallucinated text.