Traditional OCR 1.0 on PDF/image files returning text/PDF
Analyze scanned documents to detect and label content
Extract text from images using OCR
Process text to extract entities and details
Extract and query terms from documents
GOT - OCR (from : UCAS, Beijing)
Perform OCR, translate, and answer questions from documents
Extract text from images using OCR
Multimodal retrieval using llamaindex/vdr-2b-multi-v1
Extract PDFs and chat to get insights
Answer questions based on provided text
Search documents and retrieve relevant chunks
Search documents using text queries
Optical Character Recognition (OCR) is a powerful technology designed to extract text from scanned documents, images, and PDF files. It enables users to convert uneditable text within images into editable, searchable, and machine-readable text. OCR is widely used in various applications, including document scanning, data entry automation, and digitization of historical records.
• Text Extraction: Accurately extracts text from scanned documents, PDFs, and images.
• Multi-Format Support: Works with various file formats, including PDF, JPG, PNG, and more.
• Language Support: Recognizes text in multiple languages, enabling global usability.
• Layout Preservation: Maintains the original document's formatting, including tables and columns.
• Output Options: Provides extracted text in formats like plain text, PDF, or Word documents.
What is OCR used for?
OCR is primarily used to extract editable text from scanned documents, images, and PDFs, enabling tasks like data entry, document archiving, and text analysis.
What file formats does OCR support?
OCR supports a wide range of file formats, including PDF, JPG, PNG, BMP, and TIFF.
Why might OCR not always be 100% accurate?
OCR accuracy can vary depending on the quality of the input image, font styles, and document layout. Improving image quality or using advanced OCR tools can enhance accuracy.