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?

- Natural Language Processing (NLP) Advanced Guide
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Accelerated Vector Search
- Advanced Techniques in Vector Database Management
- Information Retrieval 101
- 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
How are transformer models being used for audio search applications?
Transformer models are being increasingly utilized in audio search applications due to their ability to process sequence
What are the best practices for building a SaaS platform?
Building a successful Software as a Service (SaaS) platform involves a series of best practices that focus on user needs
What is overfitting in neural networks, and how can it be avoided?
Overfitting occurs when a neural network learns the details and noise in the training data to the extent that it negativ