Extract named entities from text
Find relevant passages in documents using semantic search
GOT - OCR (from : UCAS, Beijing)
Multimodal retrieval using llamaindex/vdr-2b-multi-v1
Extract text from images with OCR
Find similar text segments based on your query
Extract text from multilingual invoices
Extract text from document images
Employs Mistral OCR for transcribing historical data
Extract text from images using OCR
Traditional OCR 1.0 on PDF/image files returning text/PDF
Identify and extract key entities from text
Find relevant text chunks from documents based on a query
Universal Ner ITA is an advanced AI-powered tool designed to extract named entities from text. Specialized for extracting text from scanned documents, it leverages cutting-edge technology to identify and classify entities such as names, locations, organizations, and dates within unstructured or semi-structured text.
• Named Entity Recognition (NER): Identifies and categorizes named entities in text. • Scanned Document Processing: Optimized to work with scanned documents, including PDFs and images. • High Accuracy: Delivers precise entity extraction even from low-quality scans. • Multilingual Support: Works with text in multiple languages, including Italian. • Integration Ready: Can be integrated into workflows or applications for seamless entity extraction.
What types of documents does Universal Ner ITA support?
Universal Ner ITA supports a wide range of scanned documents, including PDFs, images (JPG, PNG), and digitized files.
How accurate is the entity recognition?
The tool achieves high accuracy even with low-quality scans, but results may vary depending on document clarity and formatting.
Can Universal Ner ITA work with languages other than Italian?
Yes, Universal Ner ITA supports multiple languages, making it versatile for international document processing.