Evaluating Open and Accessible Visual Language Models for Optical Character Recognition in Clinical Case Report Forms.

Paper-based case report forms remain common in clinical trials, often requiring manual transcription into electronic systems. Automating this process could reduce workload and errors, but standard approaches for text or mark recognition are limited by rigid templates and poor generalizability. Recent advances in visual language models (VLMs) offer a potential alternative by jointly processing images and text. In this study, we benchmarked three open-source, locally executable VLMs (Qwen 2.5, Mistral Small 3.1, and Granite 3.2 Vision) for information extraction from printed case report forms used in an Italian stroke trial. Tasks included identifying the form title and handwritten Record ID, extracting handwritten dates, and detecting marked checkboxes. Experiments were conducted on 80 smartphone-acquired images, simulating real-world data entry. Results showed that Qwen performed best in title recognition (91%) and date extraction (75%), while Mistral achieved higher accuracy for Record IDs (53%) and checkboxes (80%). Granite, despite being the fastest, often failed to follow output formatting instructions. Across models, handwritten fields remained particularly challenging. These findings highlight both the promise and current limitations of open VLMs for streamlining clinical research workflows.