Compact Multimodal Language Models as Robust OCR Alternatives for Noisy Textual Clinical Reports (2026.eacl-industry)
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| Challenge: | Conventional OCR systems perform poorly under noisy, real-world conditions . compact multimodal models outperform classical and neural OCR pipelines . |
| Approach: | They evaluate compact multimodal language models for transcribing noisy medical documents . they compare eight different models to find the best transcription accuracy and noise sensitivity . |
| Outcome: | The proposed models outperform classical and neural OCR pipelines in transcription accuracy, noise sensitivity, numeric accuracy and computational efficiency. |
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