High‐throughput information extraction of printed specimen labels from large‐scale digitization of entomological collections using a semi‐automated pipeline

Abstract Natural history museums curate billions of insect specimens, representing an unparalleled record of biodiversity. Although large‐scale digitization has expanded access to specimen images, extracting label metadata remains a major bottleneck, typically requiring time‐intensive manual transcription. We developed ELIE (Entomological Label Information Extraction), a modular, semi‐automated pipeline that integrates computer vision methods (including convolutional neural networks), Optical Character Recognition (OCR), and clustering algorithms to streamline label data extraction. The workflow proceeds in three stages: (1) label detection and text‐type classification (printed vs. handwritten), (2) OCR‐based text extraction from printed labels using Tesseract or Google Vision, and (3) clustering of extracted text for deduplication and targeted human validation. Benchmarking across three institutional data sets demonstrated that ELIE accurately extracted and clustered up to 98% of printed labels, achieving 94% detection accuracy and reducing manual transcription effort by up to 87%. The pipeline markedly improves the efficiency of digitization workflows while maintaining high data integrity. By integrating AI‐driven automation with minimal human oversight, ELIE enables scalable, cross‐institutional digitization of entomological collections. Its implementation holds the potential to unlock large volumes of biodiversity data for research in ecology, systematics, and conservation worldwide.