Hybrid Approaches for NER in Noisy OCR Medical Records

Named entity recognition of digitized medical records accessed through Optical Character Recognition (OCR) poses significant problems, since the character distortions, uneven formatting, and domain-specific acronyms render it quite difficult. Such artifacts worsen the quality of rule-based or machine learning models that do not perform well under such noisy conditions by retaining consistent entity extraction. The use of hybrid techniques, i.e., the combination of deterministic rule-based modules with neural convolutional models like transformer-based models, is a stable remedy to these problems. Hybrid systems show better tolerance to OCR-induced noise by combining lexicon-based rules with contextual embeddings and error correction mechanisms, and ensemble strategies to maximize precision and achieve higher recall in clinical entity extraction (diagnoses, medications, and time-related entities). The piece is an analysis of the issues related to processing OCR-generated medical text, the implementation and development of hybrid NER pipelines, their institution-agnostic scalability, and research directions, such as multimodal learning, self-supervised pretraining on noisy data, and the orchestration of large-scale healthcare systems with the help of AI.