OCR (Optical Character Recognition) data extraction involves converting text from scanned images, documents, or PDFs into machine-readable formats. The process begins by detecting text regions within an image and recognizing characters using OCR algorithms. Modern OCR systems, often powered by deep learning, can handle diverse fonts, languages, and even handwritten text. Extracted text is typically organized into structured formats, such as tables or JSON files, for further processing. Applications include digitizing invoices, automating form data entry, and enabling searchable document archives. OCR data extraction improves efficiency and accuracy in text processing workflows.
What's OCR data extraction?
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Natural Language Processing (NLP) Basics
- Getting Started with Milvus
- Natural Language Processing (NLP) Advanced Guide
- Getting Started with Zilliz Cloud
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What are the computational requirements for multimodal AI models?
Multimodal AI models require a range of computational resources to effectively process and integrate different types of
What challenges arise when integrating textual or semantic conditions?
Integrating textual or semantic conditions into a software system can present various challenges that developers must ad
How do relational databases handle geographic data?
Relational databases handle geographic data by using a variety of data types and functions tailored for spatial informat