Extract named entities from medical text
Extract text from documents or images
Extract key entities from text queries
Search information in uploaded PDFs
Extract text from images using OCR
Search documents for specific information using keywords
Extract and query terms from documents
Gemma-3 OCR App
Search and summarize documents with natural language queries
Multimodal retrieval using llamaindex/vdr-2b-multi-v1
Extract text from PDF files
Traditional OCR 1.0 on PDF/image files returning text/PDF
Upload images for accurate English / Latin OCR
Medical Ner App is a specialized tool designed to extract named entities from medical text. It helps users identify and categorize specific information such as diagnoses, medications, symptoms, and medical terms from scanned documents. This app is particularly useful for healthcare professionals, researchers, and anyone needing to process medical data efficiently.
• Named Entity Recognition (NER): Automatically identifies and categorizes medical entities in text.
• Support for Scanned Documents: Utilizes OCR (Optical Character Recognition) to extract text from scanned medical documents.
• High Accuracy: Designed to handle complex medical terminology and contexts.
• User-Friendly Interface: Easy-to-use platform for uploading documents and viewing results.
• Customizable Extraction: Allows users to define specific entities they want to extract.
1. What file formats does Medical Ner App support?
Medical Ner App supports common formats like PDF, JPG, PNG, and TIFF for scanned documents.
2. How long does the extraction process take?
Processing time depends on the document length and complexity. Scanned documents may take longer due to OCR processing.
3. Can the app handle handwritten medical notes?
While the app primarily works with typed or clearly scanned text, it may struggle with handwritten notes unless they are very legible. For best results, use clear, typed, or high-quality scanned documents.