When Low CER is Not Enough: An Analysis of Hallucinations in Vision-Language OCR Systems on Historical Uruguayan Documents

arXiv:2607.24077 · cs.CV, cs.LG, eess.IV · Submitted 2026-07-27 · Read on arXiv

cs.CV, cs.LG, eess.IV

Submitted: 2026-07-27

Updated: 2026-09-17

Code: https://github.com/camilomarino/ocr_berrutti_dataset

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 11× in this paper · explained in 2 episodes
• Hallucination — 10× in this paper · explained in 4 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Fidelity — 6× in this paper · explained in 2 episodes
• Vision-language models — 6× in this paper · explained in 2 episodes
• Character Error Rate — 5× in this paper
• Failure Modes — 5× in this paper · explained in 3 episodes
• Optical Character Recognition — 5× in this paper
• Word Error Rate — 5× in this paper
• Adaptation — 3× in this paper · explained in 1 episode
• Vision-Language Models (VLMs) — 3× in this paper · explained in 6 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• METATR: A Multilingual, Evolving Benchmark for Automatic Text Recognition
• PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
• Qianfan-OCR: A Unified End-to-End Model for Document Intelligence
• GLM-OCR Technical Report
• dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
• OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
• How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning
• CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
• An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
• Few-shot Writer Adaptation via Multimodal In-Context Learning
• HunyuanOCR Technical Report
• MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale
• DeepSeek-OCR: Contexts Optical Compression
• MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
• Democratizing the medieval English legal tradition
• Multimodal OCR: Parse Anything from Documents

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models