ClinOCR-Bench: A Comprehensive Clinical Scanned Document Dataset for Optical Character Recognition Model Evaluation
arXiv:2607.03650v1 Announce Type: cross Abstract: Extracting textual information from scanned medical documents, such as external laboratory reports and manually filled forms, has been a major challenge in modern electronic health records (EHRs). Recent advancements in vision language models (VLMs) have shown great promise over traditional OCR tools. However, at this point, most clinical OCR studies were conducted on private, institutional data. To our knowledge, there are few publicly available datasets for evaluating OCR models in the clinical domain. Furthermore, common scanning artifacts t
The proliferation of advanced vision language models (VLMs) and the increasing demand for efficient healthcare data management necessitate improved performance in clinical OCR, driving the creation of specialized datasets.
This new public dataset addresses a critical gap in clinical OCR, enabling more robust and generalized model evaluation and accelerating the deployment of AI in healthcare, which can improve patient care and operational efficiency.
The availability of ClinOCR-Bench provides a standardized benchmark for clinical OCR, allowing for transparent comparison and development of models specifically tailored to the complexities of scanned medical documents.
- · Healthcare AI developers
- · Electronic health record (EHR) providers
- · Medical research institutions
- · Patients (indirectly through better data management)
- · Traditional OCR tool vendors (if they fail to adapt)
- · Manual data entry providers in healthcare
More accurate and faster extraction of clinical data from scanned documents leads to improved healthcare informatics.
Enhanced data availability will accelerate medical research, drug discovery, and personalized medicine by providing richer, more accessible clinical insights.
The integration of such AI tools could significantly reduce administrative burden and costs in healthcare, potentially freeing up resources for direct patient care.
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