LV-ROVER-MLT: Low-Resource Maltese OCR by Synthetic Fine-Tuning and Multi-Stream Arbitration

arXiv:2607.00250 · cs.CL, cs.CV · Submitted 2026-06-30 · Read on arXiv

cs.CL, cs.CV

Submitted: 2026-06-30

Updated: 2026-08-27

Code: https://github.com/adamd1985/lv-rover-mlthttps:

Terminology

• Fine-Tuning — 18× in this paper · explained in 5 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• alpha — 5× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Verification — 4× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Recognizer Output Voting Error Reduction — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 1× in this paper
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• International Conference on Document Analysis and Recognition — 1× in this paper
• Provenance — 1× in this paper · explained in 4 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• A Benchmark of State-Space Models vs. Transformers and BiLSTM-based Models for Historical Newspaper OCR
• Synthetic Document Question Answering in Hungarian

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